From ef241de23a1993f6150e9881148e6caa5439ccc0 Mon Sep 17 00:00:00 2001 From: chenjiajia Date: Thu, 19 Dec 2024 21:10:39 +0000 Subject: [PATCH] Robot Updated at:19 Dec 2024 21:10:39 GMT --- docs/awesome/Awesome-Bioinformatics.md | 352 ++++++++++--------- docs/awesome/awesome-agi-cocosci.md | 2 +- docs/awesome/awesome-angular.md | 2 + docs/awesome/awesome-ble.md | 1 + docs/awesome/awesome-chatgpt.md | 1 + docs/awesome/awesome-cpp.md | 2 + docs/awesome/awesome-css-frameworks.md | 40 --- docs/awesome/awesome-db-tools.md | 4 + docs/awesome/awesome-opentofu.md | 2 +- docs/awesome/awesome-r-learning-resources.md | 283 +++++++-------- docs/awesome/awesome-rust.md | 1 + docs/awesome/awesome-testing.md | 1 + 12 files changed, 333 insertions(+), 358 deletions(-) diff --git a/docs/awesome/Awesome-Bioinformatics.md b/docs/awesome/Awesome-Bioinformatics.md index 4cc5e8c214..29eee17e5e 100644 --- a/docs/awesome/Awesome-Bioinformatics.md +++ b/docs/awesome/Awesome-Bioinformatics.md @@ -1,9 +1,9 @@
## Awesome Bioinformatics [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) [![Check URLs](https://github.com/danielecook/Awesome-Bioinformatics/actions/workflows/url-check.yml/badge.svg)](https://github.com/danielecook/Awesome-Bioinformatics/actions/workflows/url-check.yml) [![Generate TOC](https://github.com/danielecook/Awesome-Bioinformatics/actions/workflows/toc.yml/badge.svg)](https://github.com/danielecook/Awesome-Bioinformatics/actions/workflows/toc.yml) - > 生物信息学是一个跨学科领域，它开发用于理解生物数据的方法和软件工具. — [Wikipedia](https://en.wikipedia.org/wiki/Bioinformatics) +> Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. — [Wikipedia](https://en.wikipedia.org/wiki/Bioinformatics) -精选的生物信息学软件、资源和库列表. 主要基于命令行，免费或开源. 请随时 [contribute](https://github.com/danielecook/Awesome-Bioinformatics/blob/master/CONTRIBUTING.md)! +A curated list of awesome Bioinformatics software, resources, and libraries. Mostly command line based, and free or open-source. Please feel free to [contribute](https://github.com/danielecook/Awesome-Bioinformatics/blob/master/CONTRIBUTING.md)! @@ -15,238 +15,240 @@ ## Package suites -软件包套件收集特定语言或平台的软件包和安装工具. 我们有一些用于生物信息学的软件. +Package suites gather software packages and installation tools for specific languages or platforms. We have some for bioinformatics software. -- **[Bioperl](https://github.com/bioperl/bioperl-live)** - 用于生物信息学、基因组学和生命科学的开源 Perl 工具的用户和开发者国际协会. [ [paper-2002](https://doi.org/10.1101%2Fgr.361602) | [web](https://bioperl.org) ] +- **[Bioperl](https://github.com/bioperl/bioperl-live)** - International association of users & developers of open source Perl tools for bioinformatics, genomics and life sciences. [ [paper-2002](https://doi.org/10.1101%2Fgr.361602) | [web](https://bioperl.org) ] -- **[Bioconductor](https://github.com/Bioconductor)** - 大量用于分析和理解高通量基因组数据的工具，包括 1500 多个软件包. [ [paper-2004](https://link.springer.com/article/10.1186/gb-2004-5-10-r80) | [web](https://www.bioconductor.org) ] +- **[Bioconductor](https://github.com/Bioconductor)** - A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [ [paper-2004](https://link.springer.com/article/10.1186/gb-2004-5-10-r80) | [web](https://www.bioconductor.org) ] -- **[Biopython](https://github.com/biopython/biopython) ** - 免费提供的 Python 生物计算工具，包括说明书、包装和详尽的文档. 的一部分 [Open Bioinformatics Foundation](http://open-bio.org/) . 包含非常有用 [Entrez](https://biopython.org/DIST/docs/api/Bio.Entrez-module.html) 用于访问 NCBI 数据库的 API 包. [ [paper-2009](https://pubmed.ncbi.nlm.nih.gov/19304878) | [web](https://biopython.org) ] +- **[Biopython](https://github.com/biopython/biopython)** - Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the [Open Bioinformatics Foundation](http://open-bio.org/). Contains the very useful [Entrez](https://biopython.org/DIST/docs/api/Bio.Entrez-module.html) package for API access to the NCBI databases. [ [paper-2009](https://pubmed.ncbi.nlm.nih.gov/19304878) | [web](https://biopython.org) ] -- **[Bioconda](https://github.com/bioconda)** - 一个频道 [conda package manager](http://conda.pydata.org/docs/intro.html) 专注于生物信息学软件. 包括一个存储库，其中包含 3000 多个可随时安装（使用 `conda install`）的生物信息学包. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/29967506) | [web](https://bioconda.github.io) ] +- **[Bioconda](https://github.com/bioconda)** - A channel for the [conda package manager](http://conda.pydata.org/docs/intro.html) specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with `conda install`) bioinformatics packages. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/29967506) | [web](https://bioconda.github.io) ] -- **[BioJulia](https://github.com/BioJulia)** - Julia 编程语言的生物信息学和计算生物学基础设施. [ [web](https://biojulia.net) ] -- **[Rust-Bio](https://github.com/rust-bio/rust-bio)** - 对生物信息学有用的算法和数据结构的 Rust 实现. [ [paper-2016](http://bioinformatics.oxfordjournals.org/content/early/2015/10/06/bioinformatics.btv573.short?rss=1) ] -- **[SeqAn](https://github.com/seqan/seqan3)** - 用于序列分析的现代 C++ 库. -- **[(Poly)merase](https://github.com/TimothyStiles/poly)** - 用于工程生物的 Go 库和命令行实用程序. -- **[Biocaml](https://github.com/biocaml/biocaml)** - Biocaml 旨在成为生物信息学的高性能用户友好库. +- **[BioJulia](https://github.com/BioJulia)** - Bioinformatics and computational biology infastructure for the Julia programming language. [ [web](https://biojulia.net) ] +- **[Rust-Bio](https://github.com/rust-bio/rust-bio)** - Rust implementations of algorithms and data structures useful for bioinformatics. [ [paper-2016](http://bioinformatics.oxfordjournals.org/content/early/2015/10/06/bioinformatics.btv573.short?rss=1) ] +- **[SeqAn](https://github.com/seqan/seqan3)** - The modern C++ library for sequence analysis. +- **[(Poly)merase](https://github.com/TimothyStiles/poly)** - A Go library and command line utility for engineering organisms. +- **[Biocaml](https://github.com/biocaml/biocaml)** - Biocaml aims to be a high-performance user-friendly library for Bioinformatics. ## Data Tools ### Downloading -- **[GGD](https://github.com/gogetdata/ggd-cli) ** - 去获取数据； 用于获取基因组数据的命令行界面. [ [web](https://gogetdata.github.io) ] -- **[SRA-Explorer](https://github.com/ewels/sra-explorer)** - 轻松获取 SRA 下载链接和其他信息. [ [web](https://sra-explorer.info) ] +- **[GGD](https://github.com/gogetdata/ggd-cli)** - Go Get Data; A command line interface for obtaining genomic data. [ [web](https://gogetdata.github.io) ] +- **[SRA-Explorer](https://github.com/ewels/sra-explorer)** - Easily get SRA download links and other information. [ [web](https://sra-explorer.info) ] ### Compressing -- **[Genozip](https://github.com/divonlan/genozip)** - 常见基因组文件格式（BAM、CRAM、FASTQ、VCF 等）的压缩器. [ [web](https://genozip.com/?utm_source=Awesome-Bioinformatics) | [paper-2021](https://www.researchgate.net/publication/349347156_Genozip_-_A_Universal_Extensible_Genomic_Data_Compressor) ] +- **[Genozip](https://github.com/divonlan/genozip)** - A compressor of common genomic file formats (BAM, CRAM, FASTQ, VCF etc). [ [web](https://genozip.com/?utm_source=Awesome-Bioinformatics) | [paper-2021](https://www.researchgate.net/publication/349347156_Genozip_-_A_Universal_Extensible_Genomic_Data_Compressor) ] ## Data Processing ### Command Line Utilities -- **[Bioinformatics One Liners](https://github.com/stephenturner/oneliners)** - 有用的单行命令的 Git 仓库. -- **[BioNode](https://github.com/bionode/bionode)** - 模块化和通用生物信息学，Bionode 为生物信息学分析工作流程提供可管道化的 UNIX 命令行工具和 JavaScript API. [ [web](http://bionode.io) ] -- **[bioSyntax](https://github.com/bioSyntax/bioSyntax)** - vim/less/gedit/sublime 中计算生物学文件格式（SAM、VCF、GTF、FASTA、PDB 等）的语法突出显示. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/30134911) | [web](http://www.bioSyntax.org) ] -- **[CSVKit](https://github.com/wireservice/csvkit)** - 用于处理 CSV/制表符分隔文件的实用程序. [ [web](https://csvkit.readthedocs.io/en/latest) ] -- **[csvtk](https://github.com/shenwei356/csvtk)** - 另一个跨平台、高效、实用且漂亮的 CSV/TSV 工具包. [ [web](https://bioinf.shenwei.me/csvtk) ] -- **[datamash](https://git.savannah.gnu.org/gitweb/?p=datamash.git)** - 数据转换和统计. [ [web](http://www.gnu.org/software/datamash) ] -- **[easy_qsub](https://github.com/shenwei356/easy_qsub) ** - 使用脚本模板轻松提交 PBS 作业. 支持多个输入文件. -- **GNU Parallel** - 在单个多核机器上并行运行作业的通用并行器. [Here](https://www.biostars.org/p/63816/) 是一些使用 GNU Parallel 的示例脚本. [ [web](http://www.gnu.org/software/parallel) ] -- **[grabix](https://github.com/arq5x/grabix)** - 一个用于随机访问 BGZF 文件的小工具. -- **[gsort](https://github.com/brentp/gsort)** - 根据指定顺序对基因组文件进行排序. -- **[tabix](https://github.com/samtools/tabix)** - 表文件索引. [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21208982) ] -- **[wormtable](https://github.com/wormtable/wormtable)** - 大型数据集的一次写入多次读取表. -- **[zindex](https://github.com/mattgodbolt/zindex)** - 在压缩文本文件上创建索引. +- **[Bioinformatics One Liners](https://github.com/stephenturner/oneliners)** - Git repo of useful single line commands. +- **[BioNode](https://github.com/bionode/bionode)** - Modular and universal bioinformatics, Bionode provides pipeable UNIX command line tools and JavaScript APIs for bioinformatics analysis workflows. [ [web](http://bionode.io) ] +- **[bioSyntax](https://github.com/bioSyntax/bioSyntax)** - Syntax Highlighting for Computational Biology file formats (SAM, VCF, GTF, FASTA, PDB, etc...) in vim/less/gedit/sublime. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/30134911) | [web](http://www.bioSyntax.org) ] +- **[CSVKit](https://github.com/wireservice/csvkit)** - Utilities for working with CSV/Tab-delimited files. [ [web](https://csvkit.readthedocs.io/en/latest) ] +- **[csvtk](https://github.com/shenwei356/csvtk)** - Another cross-platform, efficient, practical and pretty CSV/TSV toolkit. [ [web](https://bioinf.shenwei.me/csvtk) ] +- **[datamash](https://git.savannah.gnu.org/gitweb/?p=datamash.git)** - Data transformations and statistics. [ [web](http://www.gnu.org/software/datamash) ] +- **[easy_qsub](https://github.com/shenwei356/easy_qsub)** - Easily submitting PBS jobs with script template. Multiple input files supported. +- **GNU Parallel** - General parallelizer that runs jobs in parallel on a single multi-core machine. [Here](https://www.biostars.org/p/63816/) are some example scripts using GNU Parallel. [ [web](http://www.gnu.org/software/parallel) ] +- **[grabix](https://github.com/arq5x/grabix)** - A wee tool for random access into BGZF files. +- **[gsort](https://github.com/brentp/gsort)** - Sort genomic files according to a specified order. +- **[tabix](https://github.com/samtools/tabix)** - Table file index. [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21208982) ] +- **[wormtable](https://github.com/wormtable/wormtable)** - Write-once-read-many table for large datasets. +- **[zindex](https://github.com/mattgodbolt/zindex)** - Create an index on a compressed text file. ## Next Generation Sequencing ### Workflow Managers -- **[BigDataScript](https://github.com/pcingola/BigDataScript)** - 一种跨系统脚本语言，用于处理不同规模和功能的计算机系统中的大数据管道. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25189778) | [web](https://pcingola.github.io/BigDataScript) ] -- **[Bpipe](https://github.com/ssadedin/bpipe)** - 一种用于定义流水线阶段并将它们链接在一起以制作流水线的小型语言. [ [web](http://docs.bpipe.org) ] -- **[Common Workflow Language](https://github.com/common-workflow-language/common-workflow-language)** - 一种描述分析工作流程和工具的规范，这些工作流程和工具可移植并可扩展到各种软件和硬件环境，从工作站到集群、云和高性能计算 (HPC) 环境. [ [web](http://www.commonwl.org) ] -- **[Cromwell](https://github.com/broadinstitute/cromwell)** - 面向科学工作流程的工作流程管理系统. [ [web](https://cromwell.readthedocs.io) ] -- **[Galaxy](https://github.com/galaxyproject) ** - 一个流行的开源、基于网络的平台，用于数据密集型生物医学研究. 具有多种功能，从数据分析到工作流管理再到可视化工具. [ [paper-2018](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030816) | [web](https://galaxyproject.org) ] -- **[Nextflow](https://github.com/nextflow-io/nextflow) （推荐）** - 围绕 UNIX 管道概念建模的流畅 DSL，以可移植的方式简化了并行和可扩展管道的编写. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/29412134) | [web](http://nextflow.io) ] -- **[redun](https://github.com/insitro/redun)** - 基于 python 的工作流管理器. -- **[Ruffus](https://github.com/cgat-developers/ruffus)** - 广泛用于科学和生物信息学的 python 计算管道库. [ [paper-2010](https://pubmed.ncbi.nlm.nih.gov/20847218) | [web](http://www.ruffus.org.uk) ] -- **[SciPipe](https://github.com/scipipe/scipipe)** - 嵌入在 Go 编程语言中的工作流库，专注于支持复杂的工作流构造，编译为单个二进制文件，为每个输出提供强大的文件命名和全面的审计报告 [ [paper-2019](https://pubmed.ncbi.nlm.nih.gov/31029061/) | [web](https://scipipe.org/) ] -- **[SeqWare](https://github.com/SeqWare/seqware)** - 基于 Hadoop Oozie 的工作流系统，专注于云环境中的基因组学数据分析. [ [paper-2010](https://pubmed.ncbi.nlm.nih.gov/21210981) | [web](https://seqware.github.io) ] -- **[Snakemake](https://bitbucket.org/snakemake)** - Python 中的工作流管理系统，旨在通过提供快速舒适的执行环境来降低创建工作流的复杂性. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/29788404) | [web](https://snakemake.readthedocs.io) ] -- **[Workflow Descriptor Language](https://github.com/broadinstitute/wdl)** - 由 Broad 开发的工作流程标准. [ [web](https://software.broadinstitute.org/wdl) ] +- **[BigDataScript](https://github.com/pcingola/BigDataScript)** - A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25189778) | [web](https://pcingola.github.io/BigDataScript) ] +- **[Bpipe](https://github.com/ssadedin/bpipe)** - A small language for defining pipeline stages and linking them together to make pipelines. [ [web](http://docs.bpipe.org) ] +- **[Common Workflow Language](https://github.com/common-workflow-language/common-workflow-language)** - a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [ [web](http://www.commonwl.org) ] +- **[Cromwell](https://github.com/broadinstitute/cromwell)** - A Workflow Management System geared towards scientific workflows. [ [web](https://cromwell.readthedocs.io) ] +- **[Galaxy](https://github.com/galaxyproject)** - a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [ [paper-2018](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030816) | [web](https://galaxyproject.org) ] +- **[Nextflow](https://github.com/nextflow-io/nextflow) (recommended)** - A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/29412134) | [web](http://nextflow.io) ] +- **[redun](https://github.com/insitro/redun)** - A python-based workflow manager. +- **[Ruffus](https://github.com/cgat-developers/ruffus)** - Computation Pipeline library for python widely used in science and bioinformatics. [ [paper-2010](https://pubmed.ncbi.nlm.nih.gov/20847218) | [web](http://www.ruffus.org.uk) ] +- **[SciPipe](https://github.com/scipipe/scipipe)** - Workflow library embedded in the Go programming language, focusing on supporting complex workflow constructs, compiling to a single binary, providing powerful file naming and comprehensive audit reports for every output [ [paper-2019](https://pubmed.ncbi.nlm.nih.gov/31029061/) | [web](https://scipipe.org/) ] +- **[SeqWare](https://github.com/SeqWare/seqware)** - Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [ [paper-2010](https://pubmed.ncbi.nlm.nih.gov/21210981) | [web](https://seqware.github.io) ] +- **[Snakemake](https://bitbucket.org/snakemake)** - A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/29788404) | [web](https://snakemake.readthedocs.io) ] +- **[Workflow Descriptor Language](https://github.com/broadinstitute/wdl)** - Workflow standard developed by the Broad. [ [web](https://software.broadinstitute.org/wdl) ] ### Pipelines -- **[Awesome-Pipeline](https://github.com/pditommaso/awesome-pipeline)** - 管道资源列表. -- **[Bactopia](https://github.com/bactopia/bactopia/)** - 使用 Nextflow 构建的灵活管道，用于细菌基因组的完整分析. [ [web](https://bactopia.github.io/) ] -- **[Bacannot](https://github.com/fmalmeida/bacannot)** - 一个通用但全面的细菌注释管道，使用 Nextflow 构建，具有用于调查结果的漂亮图形选项. [ [web](https://bacannot.readthedocs.io/en/latest/?badge=latest) ] -- **[bcbio-nextgen](https://github.com/chapmanb/bcbio-nextgen)** - 电池包括用于变体和 RNA-Seq 分析、结构变体调用、注释和预测的基因组分析管道. [ [web](https://bcbio-nextgen.readthedocs.io) ] -- **[R-Peridot](https://github.com/pentalpha/r-peridot)** - 使用直观的 GUI 进行差异表达分析的可定制管道. [ [web](http://www.bioinformatics-brazil.org/r-peridot) ] -- **[ngs-preprocess](https://github.com/fmalmeida/ngs-preprocess)** - 用于预处理短序列和长序列读取的管道，使用 Nextflow 构建. [ [web](https://ngs-preprocess.readthedocs.io/en/latest/?badge=latest) ] +- **[Awesome-Pipeline](https://github.com/pditommaso/awesome-pipeline)** - A list of pipeline resources. +- **[Bactopia](https://github.com/bactopia/bactopia/)** - A flexible pipeline, built with Nextflow, for the complete analysis of bacterial genomes. [ [web](https://bactopia.github.io/) ] +- **[Bacannot](https://github.com/fmalmeida/bacannot)** - A generic but comprehensive bacterial annotation pipeline, built with Nextflow, with nice graphical options for investigating results. [ [web](https://bacannot.readthedocs.io/en/latest/?badge=latest) ] +- **[bcbio-nextgen](https://github.com/chapmanb/bcbio-nextgen)** - Batteries included genomic analysis pipeline for variant and RNA-Seq analysis, structural variant calling, annotation, and prediction. [ [web](https://bcbio-nextgen.readthedocs.io) ] +- **[R-Peridot](https://github.com/pentalpha/r-peridot)** - Customizable pipeline for differential expression analysis with an intuitive GUI. [ [web](http://www.bioinformatics-brazil.org/r-peridot) ] +- **[ngs-preprocess](https://github.com/fmalmeida/ngs-preprocess)** - A pipeline for preprocessing short and long sequencing reads, built with Nextflow. [ [web](https://ngs-preprocess.readthedocs.io/en/latest/?badge=latest) ] ### Sequence Processing -序列处理包括诸如多路分解原始读取数据和修剪低质量碱基等任务. +Sequence Processing includes tasks such as demultiplexing raw read data, and trimming low quality bases. -- **[AfterQC](https://github.com/OpenGene/AfterQC)** - fastq 数据的自动过滤、修剪、错误删除和质量控制. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/28361673) ] -- **[FastQC](https://github.com/s-andrews/FastQC)** - 用于高通量序列数据的质量控制工具. [ [web](http://www.bioinformatics.babraham.ac.uk/projects/fastqc) ] -- **[Fastqp](https://github.com/mdshw5/fastqp)** - 使用 Python 的 FASTQ 和 SAM 质量控制. -- **[Fastx Tookit](https://github.com/agordon/fastx_toolkit)** - FASTQ/A short-reads 预处理工具：解复用、修剪、裁剪、质量过滤和屏蔽实用程序. [ [web](http://hannonlab.cshl.edu/fastx_toolkit) ] -- **[MultiQC](https://github.com/ewels/MultiQC)** - 将多个样本的生物信息学分析结果汇总到一份报告中. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27312411) | [web](http://multiqc.info) ] -- **[SeqFu](https://github.com/telatin/seqfu2)** - 用 Nim 编写的用于 FASTA/FASTQ 文件的序列操作工具包. [ [paper-2021](https://www.mdpi.com/2306-5354/8/5/59) | [web](https://telatin.github.io/seqfu2/) ] -- **[SeqKit](https://github.com/shenwei356/seqkit)** - 在 Golang 中用于 FASTA/Q 文件操作的跨平台和超快工具包. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27706213) | [web](https://bioinf.shenwei.me/seqkit) ] -- **[seqmagick](https://github.com/fhcrc/seqmagick)** - 在 Biopython 中以一种方便的方式进行文件格式转换. [ [web](http://seqmagick.readthedocs.io) ] -- **[Seqtk](https://github.com/lh3/seqtk)** - 用于处理 FASTA/Q 格式序列的工具包. -- **[smof](https://github.com/incertae-sedis/smof)** - UNIX 风格的 FASTA 操作工具. +- **[AfterQC](https://github.com/OpenGene/AfterQC)** - Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/28361673) ] +- **[FastQC](https://github.com/s-andrews/FastQC)** - A quality control tool for high throughput sequence data. [ [web](http://www.bioinformatics.babraham.ac.uk/projects/fastqc) ] +- **[Fastqp](https://github.com/mdshw5/fastqp)** - FASTQ and SAM quality control using Python. +- **[Fastx Tookit](https://github.com/agordon/fastx_toolkit)** - FASTQ/A short-reads pre-processing tools: Demultiplexing, trimming, clipping, quality filtering, and masking utilities. [ [web](http://hannonlab.cshl.edu/fastx_toolkit) ] +- **[MultiQC](https://github.com/ewels/MultiQC)** - Aggregate results from bioinformatics analyses across many samples into a single report. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27312411) | [web](http://multiqc.info) ] +- **[SeqFu](https://github.com/telatin/seqfu2)** - Sequence manipulation toolkit for FASTA/FASTQ files written in Nim. [ [paper-2021](https://www.mdpi.com/2306-5354/8/5/59) | [web](https://telatin.github.io/seqfu2/) ] +- **[SeqKit](https://github.com/shenwei356/seqkit)** - A cross-platform and ultrafast toolkit for FASTA/Q file manipulation in Golang. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27706213) | [web](https://bioinf.shenwei.me/seqkit) ] +- **[seqmagick](https://github.com/fhcrc/seqmagick)** - file format conversion in Biopython in a convenient way. [ [web](http://seqmagick.readthedocs.io) ] +- **[Seqtk](https://github.com/lh3/seqtk)** - Toolkit for processing sequences in FASTA/Q formats. +- **[smof](https://github.com/incertae-sedis/smof)** - UNIX-style FASTA manipulation tools. ### Data Analysis -以下项目允许通过引入专门的数据库进行可扩展的基因组分析. +The following items allow for scalable genomic analysis by introducing specialized databases. -- **[Hail](https://github.com/hail-is/hail)** - 可扩展的基因组分析. -- **[GLNexus](https://github.com/dnanexus-rnd/GLnexus)** - 用于群体测序项目的可扩展 gVCF 合并和联合变体调用. [ [paper-2018](https://www.biorxiv.org/content/10.1101/343970v1.abstract) ] +- **[Hail](https://github.com/hail-is/hail)** - Scalable genomic analysis. +- **[GLNexus](https://github.com/dnanexus-rnd/GLnexus)** - Scalable gVCF merging and joint variant calling for population sequencing projects. [ [paper-2018](https://www.biorxiv.org/content/10.1101/343970v1.abstract) ] ### Sequence Alignment #### Pairwise -- **[Bowtie 2](https://github.com/BenLangmead/bowtie2)** - 一种超快且内存高效的工具，用于将测序读取与长参考序列对齐. [ [paper-2012](https://pubmed.ncbi.nlm.nih.gov/22388286) | [web](http://bowtie-bio.sourceforge.net/bowtie2) ] -- **[BWA](https://github.com/lh3/bwa)** - 用于 DNA 序列之间成对比对的 Burrow-Wheeler Aligner. -- **[WFA](https://github.com/smarco/WFA)** - 波前比对算法 (WFA)，它利用序列相似性来加速比对 [ [paper-2020](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa777/5904262) ] -- **[Parasail](https://github.com/jeffdaily/parasail)** - 用于全局、半全局和局部成对序列比对的 SIMD C 库 [ [paper-2016](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0930-z) ] -- **[MUMmer](https://github.com/mummer4/mummer)** - 一个用于快速比对整个基因组的系统，无论是完整的还是草稿形式. [ [paper-1999](http://mummer.sourceforge.net/MUMmer.pdf) | [paper-2002](http://mummer.sourceforge.net/MUMmer2.pdf) | [paper-2004](http://mummer.sourceforge.net/MUMmer3.pdf) | [web](http://mummer.sourceforge.net) ] -- **[DIAMOND](https://github.com/bbuchfink/diamond)** - 一种超快蛋白质对齐器，用于类似搜索的“blastp”和“blastx”. [ [paper-2021](https://www.nature.com/articles/s41592-021-01101-x) ] +- **[Bowtie 2](https://github.com/BenLangmead/bowtie2)** - An ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. [ [paper-2012](https://pubmed.ncbi.nlm.nih.gov/22388286) | [web](http://bowtie-bio.sourceforge.net/bowtie2) ] +- **[BWA](https://github.com/lh3/bwa)** - Burrow-Wheeler Aligner for pairwise alignment between DNA sequences. +- **[WFA](https://github.com/smarco/WFA)** - the wavefront alignment algorithm (WFA) which expoit sequence similarity to speed up alignment [ [paper-2020](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa777/5904262) ] +- **[Parasail](https://github.com/jeffdaily/parasail)** - SIMD C library for global, semi-global, and local pairwise sequence alignments [ [paper-2016](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0930-z) ] +- **[MUMmer](https://github.com/mummer4/mummer)** - A system for rapidly aligning entire genomes, whether in complete or draft form. [ [paper-1999](http://mummer.sourceforge.net/MUMmer.pdf) | [paper-2002](http://mummer.sourceforge.net/MUMmer2.pdf) | [paper-2004](http://mummer.sourceforge.net/MUMmer3.pdf) | [web](http://mummer.sourceforge.net) ] +- **[DIAMOND](https://github.com/bbuchfink/diamond)** - An ultrafast protein aligner for `blastp` and `blastx` like searches. [ [paper-2021](https://www.nature.com/articles/s41592-021-01101-x) ] #### Multiple Sequence Alignment -- **[POA](https://github.com/ljdursi/poapy)** - 用于多个同源序列的快速比对和共识的部分顺序比对. [ [paper-2002](https://academic.oup.com/bioinformatics/article/18/3/452/236691) ] +- **[POA](https://github.com/ljdursi/poapy)** - Partial-Order Alignment for fast alignment and consensus of multiple homologous sequences. [ [paper-2002](https://academic.oup.com/bioinformatics/article/18/3/452/236691) ] #### Clustering -- **[MMseqs2](https://github.com/soedinglab/MMseqs2)** - 用于蛋白质和核苷酸序列集的超快速、灵敏的搜索和聚类套件. [ [paper-2017](https://www.nature.com/articles/nbt.3988) | [paper-2018](https://www.nature.com/articles/s41467-018-04964-5) ] +- **[MMseqs2](https://github.com/soedinglab/MMseqs2)** - Ultra-fast, sensitive search and clustering suite for protein and nucleotide sequence sets. [ [paper-2017](https://www.nature.com/articles/nbt.3988) | [paper-2018](https://www.nature.com/articles/s41467-018-04964-5) ] ### Quantification -- **[Cufflinks](https://github.com/cole-trapnell-lab/cufflinks)** - Cufflinks 组装转录本，估计它们的丰度，并测试 RNA-Seq 样本中的差异表达和调节. [ [paper-2010](https://www.nature.com/articles/nbt.1621) ] -- **[RSEM](https://github.com/deweylab/RSEM)** - 用于从 RNA-Seq 数据估计基因和亚型表达水平的软件包. [ [paper-2011](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-323) | [web](http://deweylab.github.io/RSEM/) ] +- **[Cufflinks](https://github.com/cole-trapnell-lab/cufflinks)** - Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. [ [paper-2010](https://www.nature.com/articles/nbt.1621) ] +- **[RSEM](https://github.com/deweylab/RSEM)** - A software package for estimating gene and isoform expression levels from RNA-Seq data. [ [paper-2011](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-323) | [web](http://deweylab.github.io/RSEM/) ] ### Variant Calling -- **[DeepVariant](https://github.com/google/deepvariant)** - 基于深度学习的变体调用者 [ [paper-2018](https://rdcu.be/7Dhl) ] -- **[freebayes](https://github.com/ekg/freebayes)** - 基于贝叶斯单倍型的多态性发现和基因分型. [ [web](http://arxiv.org/abs/1207.3907) ] -- **[GATK](https://github.com/broadgsa/gatk)** - 高通量测序数据中的变体发现. [ [web](https://software.broadinstitute.org/gatk) ] -- **[Octopus](https://github.com/luntergroup/octopus)** - 具有广泛适用性的多态贝叶斯基因分型模型. [ [paper-2021](https://www.nature.com/articles/s41587-021-00861-3) ] -- **[bcftools](https://github.com/samtools/bcftools)** - samtools/bcftools 是一套用于操作 NGS 数据的工具，可用于调用变体. [ [paper-2009](https://pubmed.ncbi.nlm.nih.gov/19505943) | [web](http://htslib.org) ] +- **[DeepVariant](https://github.com/google/deepvariant)** - Deep learning-based variant caller [ [paper-2018](https://rdcu.be/7Dhl) ] +- **[freebayes](https://github.com/ekg/freebayes)** - Bayesian haplotype-based polymorphism discovery and genotyping. [ [web](http://arxiv.org/abs/1207.3907) ] +- **[GATK](https://github.com/broadgsa/gatk)** - Variant Discovery in High-Throughput Sequencing Data. [ [web](https://software.broadinstitute.org/gatk) ] +- **[Octopus](https://github.com/luntergroup/octopus)** - A polymorphic bayesian genotyping model with wide applicability. [ [paper-2021](https://www.nature.com/articles/s41587-021-00861-3) ] +- **[bcftools](https://github.com/samtools/bcftools)** - samtools/bcftools are a suite of tools for manipulating NGS data and can be used to call variants. [ [paper-2009](https://pubmed.ncbi.nlm.nih.gov/19505943) | [web](http://htslib.org) ] #### Structural variant callers -- **[Delly](https://github.com/dellytools/delly)** - 通过集成的配对末端和拆分读取分析发现结构变异. [ [paper-2012](https://pubmed.ncbi.nlm.nih.gov/22962449) ] -- **[lumpy](https://github.com/arq5x/lumpy-sv)** - lumpy：结构变异发现的通用概率框架. [ [paper-2014](https://link.springer.com/article/10.1186/gb-2014-15-6-r84) ] -- **[manta](https://github.com/Illumina/manta)** - 映射测序数据的结构变体和插入缺失调用者. [ [paper-2015](https://pubmed.ncbi.nlm.nih.gov/26647377) ] -- **[gridss](https://github.com/PapenfussLab/gridss)** - GRIDSS：基因组重排识别软件套件. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/29097403) ] -- **[smoove](https://github.com/brentp/smoove)** - 使用现有工具进行结构变异检出和基因分型，但很顺利. +- **[Delly](https://github.com/dellytools/delly)** - Structural variant discovery by integrated paired-end and split-read analysis. [ [paper-2012](https://pubmed.ncbi.nlm.nih.gov/22962449) ] +- **[lumpy](https://github.com/arq5x/lumpy-sv)** - lumpy: a general probabilistic framework for structural variant discovery. [ [paper-2014](https://link.springer.com/article/10.1186/gb-2014-15-6-r84) ] +- **[manta](https://github.com/Illumina/manta)** - Structural variant and indel caller for mapped sequencing data. [ [paper-2015](https://pubmed.ncbi.nlm.nih.gov/26647377) ] +- **[gridss](https://github.com/PapenfussLab/gridss)** - GRIDSS: the Genomic Rearrangement IDentification Software Suite. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/29097403) ] +- **[smoove](https://github.com/brentp/smoove)** - structural variant calling and genotyping with existing tools, but,smoothly. ### BAM File Utilities -- **[Bamtools](https://github.com/pezmaster31/bamtools)** - 用于处理 BAM 文件的工具集合. [ [paper-2011](https://academic.oup.com/bioinformatics/article/27/12/1691/255399) ] -- **[bam toolbox](https://github.com/AndersenLab/bam-toolbox) ** MtDNA：核覆盖； BAM 工具箱可以输出 MtDNA 的比率：核覆盖率，代表线粒体含量. -- **[mergesam](https://github.com/DarwinAwardWinner/mergesam)** - 自动执行常见的 SAM 和 BAM 转换. -- **[mosdepth](https://github.com/brentp/mosdepth)** - 用于 WGS、外显子组或靶向测序的快速 BAM/CRAM 深度计算. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/29096012/) ] -- **[SAMstat](https://github.com/TimoLassmann/samstat)** - 显示下一代测序的序列统计信息. [ [paper-2010](https://academic.oup.com/bioinformatics/article/27/1/130/201972) | [web](http://samstat.sourceforge.net) ] -- **[Somalier](https://github.com/brentp/somalier)** - 对 BAM/CRAM/VCF/GVCF 进行快速样本交换和相关性检查. [ [paper-2020](https://pubmed.ncbi.nlm.nih.gov/32664994) ] -- **[Telseq](https://github.com/zd1/telseq)** - Telseq 是一种从全基因组序列数据中估算端粒长度的工具. [ [paper-2014](https://academic.oup.com/nar/article/42/9/e75/1249448) ] +- **[Bamtools](https://github.com/pezmaster31/bamtools)** - Collection of tools for working with BAM files. [ [paper-2011](https://academic.oup.com/bioinformatics/article/27/12/1691/255399) ] +- **[bam toolbox](https://github.com/AndersenLab/bam-toolbox)** MtDNA:Nuclear Coverage; BAM Toolbox can output the ratio of MtDNA:nuclear coverage, a proxy for mitochondrial content. +- **[mergesam](https://github.com/DarwinAwardWinner/mergesam)** - Automate common SAM & BAM conversions. +- **[mosdepth](https://github.com/brentp/mosdepth)** - fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/29096012/) ] +- **[SAMstat](https://github.com/TimoLassmann/samstat)** - Displaying sequence statistics for next-generation sequencing. [ [paper-2010](https://academic.oup.com/bioinformatics/article/27/1/130/201972) | [web](http://samstat.sourceforge.net) ] +- **[Somalier](https://github.com/brentp/somalier)** - Fast sample-swap and relatedness checks on BAMs/CRAMs/VCFs/GVCFs. [ [paper-2020](https://pubmed.ncbi.nlm.nih.gov/32664994) ] +- **[Telseq](https://github.com/zd1/telseq)** - Telseq is a tool for estimating telomere length from whole genome sequence data. [ [paper-2014](https://academic.oup.com/nar/article/42/9/e75/1249448) ] ### VCF File Utilities -- **[bcftools](https://github.com/samtools/bcftools)** - 用于操作 VCF 文件的工具集. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/26826718) | [paper-2017](https://pubmed.ncbi.nlm.nih.gov/28205675) | [web](http://samtools.github.io/bcftools) ] -- **[vcfanno](https://github.com/brentp/vcfanno)** - 使用其他 VCF/BED/tabixed 文件注释 VCF. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27250555) ] -- **[vcflib](https://github.com/vcflib/vcflib)** - 用于解析和操作 VCF 文件的 C++ 库. -- **[vcftools](https://github.com/vcftools/vcftools)** - VCF 操作和统计（例如连锁不平衡、等位基因频率、Fst）. [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21653522) ] +- **[bcftools](https://github.com/samtools/bcftools)** - Set of tools for manipulating VCF files. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/26826718) | [paper-2017](https://pubmed.ncbi.nlm.nih.gov/28205675) | [web](http://samtools.github.io/bcftools) ] +- **[vcfanno](https://github.com/brentp/vcfanno)** - Annotate a VCF with other VCFs/BEDs/tabixed files. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27250555) ] +- **[vcflib](https://github.com/vcflib/vcflib)** - A C++ library for parsing and manipulating VCF files. +- **[vcftools](https://github.com/vcftools/vcftools)** - VCF manipulation and statistics (e.g. linkage disequilibrium, allele frequency, Fst). [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21653522) ] ### GFF BED File Utilities -- **[AGAT](https://github.com/NBISweden/AGAT)** - 处理任何 GTF/GFF 格式的基因注释的工具套件. [ [web](https://agat.readthedocs.io/en/latest/?badge=latest) ] -- **[gffutils](https://github.com/daler/gffutils)** - GFF 和 GTF 文件操作和相互转换. [ [web](http://daler.github.io/gffutils) ] -- **[BEDOPS](https://bedops.readthedocs.io/en/latest/index.html)** - 快速、高度可扩展且易于并行化的基因组分析工具包. [ [paper-2012](https://academic.oup.com/bioinformatics/article/28/14/1919/218826) ] +- **[AGAT](https://github.com/NBISweden/AGAT)** - Suite of tools to handle gene annotations in any GTF/GFF format. [ [web](https://agat.readthedocs.io/en/latest/?badge=latest) ] +- **[gffutils](https://github.com/daler/gffutils)** - GFF and GTF file manipulation and interconversion. [ [web](http://daler.github.io/gffutils) ] +- **[BEDOPS](https://bedops.readthedocs.io/en/latest/index.html)** - The fast, highly scalable and easily-parallelizable genome analysis toolkit. [ [paper-2012](https://academic.oup.com/bioinformatics/article/28/14/1919/218826) ] - **[Bedtools2](https://github.com/arq5x/bedtools2)** - A Swiss Army knife for genome arithmetic. [ [paper-2010](https://pubmed.ncbi.nlm.nih.gov/20110278) | [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25199790) | [web](https://bedtools.readthedocs.io) ] ### Variant Simulation -- **[Bam Surgeon](https://github.com/adamewing/bamsurgeon)** - 用于向现有 .bam 文件添加突变的工具，用于测试突变调用者. [ [web](https://popmodels.cancercontrol.cancer.gov/gsr/packages/bamsurgeon) ] -- **[wgsim](https://github.com/lh3/wgsim)** - **带有 samtools！** - 读取模拟器. [ [web](https://popmodels.cancercontrol.cancer.gov/gsr/packages/wgsim) ] +- **[Bam Surgeon](https://github.com/adamewing/bamsurgeon)** - Tools for adding mutations to existing `.bam` files, used for testing mutation callers. [ [web](https://popmodels.cancercontrol.cancer.gov/gsr/packages/bamsurgeon) ] +- **[wgsim](https://github.com/lh3/wgsim)** - **Comes with samtools!** - Reads simulator. [ [web](https://popmodels.cancercontrol.cancer.gov/gsr/packages/wgsim) ] ### Variant Prediction/Annotation -- **[SIFT](https://github.com/teamdfir/sift)** - 预测氨基酸取代是否影响蛋白质功能. [ [paper-2003](https://pubmed.ncbi.nlm.nih.gov/12824425) | [web](http://sift.jcvi.org) ] -- **[SnpEff](https://github.com/pcingola/SnpEff)** - 遗传变异注释和效应预测工具箱. [ [paper-2012](https://www.tandfonline.com/doi/full/10.4161/fly.19695) | [web](https://pcingola.github.io/SnpEff) ] -- **[Ensembl VEP](https://anaconda.org/bioconda/ensembl-vep)** - VEP 确定您的变异（SNP、插入、缺失、CNV 或结构变异）对基因、转录本和蛋白质序列以及调控区域的影响. [ [paper-2016](https://doi.org/10.1186/s13059-016-0974-4) | [web](http://www.ensembl.org/info/docs/tools/vep/index.html) ] +- **[SIFT](https://github.com/teamdfir/sift)** - Predicts whether an amino acid substitution affects protein function. [ [paper-2003](https://pubmed.ncbi.nlm.nih.gov/12824425) | [web](http://sift.jcvi.org) ] +- **[SnpEff](https://github.com/pcingola/SnpEff)** - Genetic variant annotation and effect prediction toolbox. [ [paper-2012](https://www.tandfonline.com/doi/full/10.4161/fly.19695) | [web](https://pcingola.github.io/SnpEff) ] +- **[Ensembl VEP](https://anaconda.org/bioconda/ensembl-vep)** - The VEP determines the effect of your variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions. [ [paper-2016](https://doi.org/10.1186/s13059-016-0974-4) | [web](http://www.ensembl.org/info/docs/tools/vep/index.html) ] ### Python Modules #### Data -- **[cruzdb](https://github.com/brentp/cruzdb)** - Pythonic 访问 UCSC 基因组数据库. [ [paper-2013](https://academic.oup.com/bioinformatics/article/29/23/3003/248468) ] -- **[pyensembl](https://github.com/openvax/pyensembl)** - Pythonic 访问 Ensembl 数据库. [ [web](https://pyensembl.readthedocs.io/en/latest/pyensembl.html) ] -- **[bioservices](https://github.com/cokelaer/bioservices)** - 从 Python 访问生物 Web 服务. [ [paper-2013](https://academic.oup.com/bioinformatics/article/29/24/3241/194040) | [web](http://bioservices.readthedocs.io) ] +- **[cruzdb](https://github.com/brentp/cruzdb)** - Pythonic access to the UCSC Genome database. [ [paper-2013](https://academic.oup.com/bioinformatics/article/29/23/3003/248468) ] +- **[pyensembl](https://github.com/openvax/pyensembl)** - Pythonic Access to the Ensembl database. [ [web](https://pyensembl.readthedocs.io/en/latest/pyensembl.html) ] +- **[bioservices](https://github.com/cokelaer/bioservices)** - Access to Biological Web Services from Python. [ [paper-2013](https://academic.oup.com/bioinformatics/article/29/24/3241/194040) | [web](http://bioservices.readthedocs.io) ] #### Tools -- **[cyvcf](https://github.com/arq5x/cyvcf)** - 一个端口 [pyVCF](https://github.com/jamescasbon/PyVCF) 使用 Cython 提高速度. -- **[cyvcf2](https://github.com/brentp/cyvcf2) ** - Cython + HTSlib == 快速 VCF 解析； 甚至比 pyVCF 更快的解析. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/28165109) | [web](https://brentp.github.io/cyvcf2) ] -- **[pyBedTools](https://github.com/daler/pybedtools)** - Python 包装器 [bedtools](https://github.com/arq5x/bedtools). [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21949271) | [web](http://daler.github.io/pybedtools) ] -- **[pyfaidx](https://github.com/mdshw5/pyfaidx)** - Pythonic 访问 FASTA 文件. -- **[pysam](https://github.com/pysam-developers/pysam)** - Python 包装器 [samtools](https://github.com/samtools/samtools). [ [web](https://pysam.readthedocs.io/en/latest/api.html) ] -- **[pyVCF](https://github.com/jamescasbon/PyVCF)** - Python 的 VCF 解析器. [ [web](http://pyvcf.readthedocs.org/en/latest/index.html) ] +- **[cyvcf](https://github.com/arq5x/cyvcf)** - A port of [pyVCF](https://github.com/jamescasbon/PyVCF) using Cython for speed. +- **[cyvcf2](https://github.com/brentp/cyvcf2)** - Cython + HTSlib == fast VCF parsing; even faster parsing than pyVCF. [ [paper-2017](https://pubmed.ncbi.nlm.nih.gov/28165109) | [web](https://brentp.github.io/cyvcf2) ] +- **[pyBedTools](https://github.com/daler/pybedtools)** - Python wrapper for [bedtools](https://github.com/arq5x/bedtools). [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21949271) | [web](http://daler.github.io/pybedtools) ] +- **[pyfaidx](https://github.com/mdshw5/pyfaidx)** - Pythonic access to FASTA files. +- **[pysam](https://github.com/pysam-developers/pysam)** - Python wrapper for [samtools](https://github.com/samtools/samtools). [ [web](https://pysam.readthedocs.io/en/latest/api.html) ] +- **[pyVCF](https://github.com/jamescasbon/PyVCF)** - A VCF Parser for Python. [ [web](http://pyvcf.readthedocs.org/en/latest/index.html) ] ### Assembly -- **[SPAdes](https://github.com/ablab/spades)** - SPAdes（圣彼得堡基因组组装器）是一个组装工具包，包含各种组装管道和原核基因组组装的事实标准. -- **[SKESA](https://github.com/ncbi/SKESA) ** - SKESA 是一种用于微生物基因组的从头序列读取组装器. 它使用保守的启发式方法，旨在在基因组的重复区域创建中断. 这导致出色的序列质量，而不会显着损害连续性. +- **[SPAdes](https://github.com/ablab/spades)** - SPAdes (St. Petersburg genome assembler) is an assembly toolkit containing various assembly pipelines and the de-facto standard for prokaryotic genome assemblies. +- **[SKESA](https://github.com/ncbi/SKESA)** - SKESA is a de-novo sequence read assembler for microbial genomes. It uses conservative heuristics and is designed to create breaks at repeat regions in the genome. This leads to excellent sequence quality without significantly compromising contiguity. +- **[Minimap2](https://github.com/lh3/minimap2)** - Minimap2 is an pairwise aligner for genomic and spliced nucleotide sequences. It can perform the assembly-to-assembly alignment, and works with gzip'd FASTQ, FASTA formats. It also finds overlaps between long-reads. +- **[D-GENIES](https://dgenies.toulouse.inra.fr/)** - **D**ot plot large **Gen**omes in an **I**nteractive, **E**fficient and **S**imple way. It is an online tool designed to support large genome, compare two genomes, and you can interact with the dot plot to improve the visualisation. It can also be used for extension of minimap2 by uploading the output generated in PAF(Pairwise mApping Format) or MAF(Multiple Alignment File) alignment files to D-GENIES ### Annotation -- **[Prokka](https://github.com/tseemann/prokka) ** - Prokka：快速原核基因组注释. Prokka 是微生物基因组注释中引用最多的注释命令行工具之一. +- **[Prokka](https://github.com/tseemann/prokka)** - Prokka: rapid prokaryotic genome annotation. Prokka is one of the most cited annotation command line tools for microbial genome annotations. - **[Bakta](https://github.com/oschwengers/bakta)** - Bakta is a tool for the rapid & standardized annotation of bacterial genomes & plasmids. It provides dbxref-rich and sORF-including annotations in machine-readable JSON & bioinformatics standard file formats for automatic downstream analysis. ## Long-read sequencing ### Long-read Assembly -- **[canu](https://github.com/marbl/canu)** - 用于大基因组和小基因组的单分子序列组装器. -- **[flye](https://github.com/fenderglass/Flye)** - 使用重复图进行单分子测序读取的从头组装器. -- **[hifiasm](https://github.com/chhylp123/hifiasm)** - 用于准确 Hifi 读取的单倍型解析汇编器. -- **[wtdbg2](https://github.com/ruanjue/wtdbg2)** - 一种用于长噪声读取组装的模糊 Bruijn 图方法 +- **[canu](https://github.com/marbl/canu)** - A single molecule sequence assembler for genomes large and small. +- **[flye](https://github.com/fenderglass/Flye)** - De novo assembler for single molecule sequencing reads using repeat graphs. +- **[hifiasm](https://github.com/chhylp123/hifiasm)** - A haplotype-resolved assembler for accurate Hifi reads. +- **[wtdbg2](https://github.com/ruanjue/wtdbg2)** - A fuzzy Bruijn graph approach to long noisy reads assembly ## Visualization ### Genome Browsers / Gene Diagrams -以下工具可用于可视化基因组数据或构建定制的基因组数据可视化，包括来自 DNA-Seq、RNA-Seq 和 ChIP-Seq、变体等的序列数据. +The following tools can be used to visualize genomic data or for constructing customized visualizations of genomic data including sequence data from DNA-Seq, RNA-Seq, and ChIP-Seq, variants, and more. -- **[Squiggle](https://github.com/Lab41/squiggle)** - 易于使用的 DNA 序列可视化工具，可将 FASTA 文件转换为基于浏览器的可视化. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/30247632) | [web](https://squiggle.readthedocs.io/en/latest/) ] -- **[biodalliance](https://github.com/dasmoth/dalliance) ** - 可嵌入的基因组查看器. 集成来自各种来源的数据，并可以直接从流行的基因组学文件格式（包括 bigWig、BAM 和 VCF）加载数据. +- **[Squiggle](https://github.com/Lab41/squiggle)** - Easy-to-use DNA sequence visualization tool that turns FASTA files into browser-based visualizations. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/30247632) | [web](https://squiggle.readthedocs.io/en/latest/) ] +- **[biodalliance](https://github.com/dasmoth/dalliance)** - Embeddable genome viewer. Integration data from a wide variety of sources, and can load data directly from popular genomics file formats including bigWig, BAM, and VCF. [ [paper-2011](https://pubmed.ncbi.nlm.nih.gov/21252075) | [web](http://www.biodalliance.org) ] -- **[BioJS](https://github.com/biojs/biojs)** - BioJS 是一个包含一百多个 JavaScript 组件的库，使您能够使用当前的 Web 技术可视化和处理数据. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25075290/) | [web](http://biojs.net/) ] -- **[Circleator](https://github.com/jonathancrabtree/Circleator)** - 使用 BioPerl 和 SVG 对基因组相关数据进行灵活的循环可视化. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25075113) ] -- **[DNAism](https://github.com/drio/dnaism)** - 基于地平线图 D3 的 DNA 数据 JavaScript 库. [ [paper-2016](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0891-2) | [web](http://drio.github.io/dnaism/) ] -- **[IGV js](https://github.com/igvteam/igv) ** - 基于 Java 的浏览器. 用于基因组数据和注释的快速、高效、可扩展的可视化工具. 处理多种格式. [ [paper-2019](https://pubmed.ncbi.nlm.nih.gov/31099383) | [web](https://software.broadinstitute.org/software/igv) ] -- **[Island Plot](https://github.com/lairdm/islandplot) ** - 基于 D3 JavaScript 的基因组查看器. 构建 SVG. [ [paper-2015](https://pubmed.ncbi.nlm.nih.gov/25916842/) ] -- **[JBrowse](https://github.com/GMOD/jbrowse)** - JavaScript 基因组浏览器，可通过插件和轨道定制进行高度定制. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27072794) | [web](http://jbrowse.org/) ] -- **[PHAT](https://github.com/chgibb/PHAT)** - 用于分析和可视化下一代测序数据集的点击式跨平台套件. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/30561651) | [web](https://chgibb.github.io/PHATDocs) ] -- **[pileup.js](https://github.com/hammerlab/pileup.js)** - JavaScript 库，可用于生成交互式和高度可定制的基于 Web 的基因组浏览器. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27153605) ] +- **[BioJS](https://github.com/biojs/biojs)** - BioJS is a library of over hundred JavaScript components enabling you to visualize and process data using current web technologies. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25075290/) | [web](http://biojs.net/) ] +- **[Circleator](https://github.com/jonathancrabtree/Circleator)** - Flexible circular visualization of genome-associated data with BioPerl and SVG. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25075113) ] +- **[DNAism](https://github.com/drio/dnaism)** - Horizon chart D3-based JavaScript library for DNA data. [ [paper-2016](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0891-2) | [web](http://drio.github.io/dnaism/) ] +- **[IGV js](https://github.com/igvteam/igv)** - Java-based browser. Fast, efficient, scalable visualization tool for genomics data and annotations. Handles a large variety of formats. [ [paper-2019](https://pubmed.ncbi.nlm.nih.gov/31099383) | [web](https://software.broadinstitute.org/software/igv) ] +- **[Island Plot](https://github.com/lairdm/islandplot)** - D3 JavaScript based genome viewer. Constructs SVGs. [ [paper-2015](https://pubmed.ncbi.nlm.nih.gov/25916842/) ] +- **[JBrowse](https://github.com/GMOD/jbrowse)** - JavaScript genome browser that is highly customizable via plugins and track customizations. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27072794) | [web](http://jbrowse.org/) ] +- **[PHAT](https://github.com/chgibb/PHAT)** - Point and click, cross platform suite for analysing and visualizing next-generation sequencing datasets. [ [paper-2018](https://pubmed.ncbi.nlm.nih.gov/30561651) | [web](https://chgibb.github.io/PHATDocs) ] +- **[pileup.js](https://github.com/hammerlab/pileup.js)** - JavaScript library that can be used to generate interactive and highly customizable web-based genome browsers. [ [paper-2016](https://pubmed.ncbi.nlm.nih.gov/27153605) ] - **[scribl](https://github.com/chmille4/Scribl)** - JavaScript library for drawing canvas-based gene diagrams. [ [paper-2012](https://pubmed.ncbi.nlm.nih.gov/23172864) | [web](http://chmille4.github.io/Scribl) ] -- **Lucid Align** - 现代序列比对查看器. [ [web](https://lucidalign.com) ] +- **Lucid Align** - A modern sequence alignment viewer. [ [web](https://lucidalign.com) ] ### Circos Related -- **[Circos](https://github.com/vigsterkr/circos)** - 圆形图的 Perl 包，非常适合基因组重排. [ [paper-2009](https://pubmed.ncbi.nlm.nih.gov/19541911) | [web](http://circos.ca) ] -- **ClicO FS** - Circos 的基于 Web 的交互式服务. [ [paper-2015](https://pubmed.ncbi.nlm.nih.gov/26227146) ] -- **OmicCircos** - 组学数据圆形图的 R 包. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/24526832) | [web](http://www.bioconductor.org/packages/release/bioc/html/OmicCircos.html) ] -- **J-Circos** - 用于与 circos 图进行交互工作的 Java 应用程序. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25540184) | [web](http://www.australianprostatecentre.org/research/software/jcircos) ] -- **[rCircos](https://bitbucket.org/henryhzhang/rcircos/src/master/)** - 用于圆形图的 R 包. [ [paper-2013](https://pubmed.ncbi.nlm.nih.gov/23937229) | [web](http://watson.nci.nih.gov/cran_mirror/web/packages/RCircos/index.html) ] -- **[fujiplot](https://github.com/mkanai/fujiplot)** - 多个 GWAS 结果的 circos 表示. [ [paper-2018](https://www.nature.com/articles/s41588-018-0047-6) ] +- **[Circos](https://github.com/vigsterkr/circos)** - Perl package for circular plots, which are well suited for genomic rearrangements. [ [paper-2009](https://pubmed.ncbi.nlm.nih.gov/19541911) | [web](http://circos.ca) ] +- **ClicO FS** - An interactive web-based service of Circos. [ [paper-2015](https://pubmed.ncbi.nlm.nih.gov/26227146) ] +- **OmicCircos** - R package for circular plots for omics data. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/24526832) | [web](http://www.bioconductor.org/packages/release/bioc/html/OmicCircos.html) ] +- **J-Circos** - A Java application for doing interactive work with circos plots. [ [paper-2014](https://pubmed.ncbi.nlm.nih.gov/25540184) | [web](http://www.australianprostatecentre.org/research/software/jcircos) ] +- **[rCircos](https://bitbucket.org/henryhzhang/rcircos/src/master/)** - R package for circular plots. [ [paper-2013](https://pubmed.ncbi.nlm.nih.gov/23937229) | [web](http://watson.nci.nih.gov/cran_mirror/web/packages/RCircos/index.html) ] +- **[fujiplot](https://github.com/mkanai/fujiplot)** - A circos representation of multiple GWAS results. [ [paper-2018](https://www.nature.com/articles/s41588-018-0047-6) ] ## Database Access -- [Entrez Direct: E-utilities on the UNIX command line](http://www.ncbi.nlm.nih.gov/books/NBK179288/) - 以编程方式访问 NCBI 数据库的 UNIX 命令行工具. 安装说明和示例可在链接中找到. +- [Entrez Direct: E-utilities on the UNIX command line](http://www.ncbi.nlm.nih.gov/books/NBK179288/) - UNIX command line tools to access NCBI's databases programmatically. Instructions to install and examples are found in the link. ## Resources @@ -255,72 +257,72 @@ - [What is a bioinformatician](http://blog.fejes.ca/?p=2418) - [Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies](http://www.ploscompbiol.org/article/info:doi%2F10.1371%2Fjournal.pcbi.1003496) - [Top N Reasons To Do A Ph.D. or Post-Doc in Bioinformatics/Computational Biology](http://caseybergman.wordpress.com/2012/07/31/top-n-reasons-to-do-a-ph-d-or-post-doc-in-bioinformaticscomputational-biology/) -- [A 10-Step Guide to Party Conversation For Bioinformaticians](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-1-104) - 以下是有关如何在被问及以下问题时向未参与该领域的人传达概念的分步指南：“那么，你是做什么的？” -- [A History Of Bioinformatics (In The Year 2039)](https://www.youtube.com/watch?v=uwsjwMO-TEA) - C. Titus Brown 关于他回顾 2039 年生物信息学的演讲.可以找到他对这次演讲的笔记 [here](http://ivory.idyll.org/blog/2014-bosc-keynote.html). -- [A farewell to bioinformatics](https://madhadron.com/science/farewell_to_bioinformatics.html) - 对生物信息学现状的批判性看法. -- [A Series of Interviews with Notable Bioinformaticians](http://www.acgt.me/blog/2014/3/25/101-questions-a-new-series-of-interviews-with-notable-bioinformaticians) - Keith Bradnam 博士“认为向一群著名的生物信息学家提出一系列简单的问题，以评估他们对生物信息学研究现状的感受，并可能获得他们对他们有用的任何提示生物信息学职业.” -- [Open Source Society University on Bioinformatics](https://github.com/ossu/bioinformatics) - 对于那些想在自己的时间免费完成生物信息学课程的人来说，这是一条坚实的道路，课程来自世界上最好的大学. -- [Rosalind](http://rosalind.info/) - Rosalind 是一个通过解决问题来学习生物信息学的平台. -- [A guide for the lonely bioinformatician](http://www.opiniomics.org/a-guide-for-the-lonely-bioinformatician/) - 本指南面向生物信息学家，旨在引导他们实现更好的职业发展. +- [A 10-Step Guide to Party Conversation For Bioinformaticians](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-1-104) - Here is a step-by-step guide on how to convey concepts to people not involved in the field when asked the question: 'So, what do you do?' +- [A History Of Bioinformatics (In The Year 2039)](https://www.youtube.com/watch?v=uwsjwMO-TEA) - A talk by C. Titus Brown on his take of looking back at bioinformatics from the year 2039. His notes for this talk can be found [here](http://ivory.idyll.org/blog/2014-bosc-keynote.html). +- [A farewell to bioinformatics](https://madhadron.com/science/farewell_to_bioinformatics.html) - A critical view of the state of bioinformatics. +- [A Series of Interviews with Notable Bioinformaticians](http://www.acgt.me/blog/2014/3/25/101-questions-a-new-series-of-interviews-with-notable-bioinformaticians) - Dr. Keith Bradnam "thought it might be instructive to ask a simple series of questions to a bunch of notable bioinformaticians to assess their feelings on the current state of bioinformatics research, and maybe get any tips they have about what has been useful to their bioinformatics careers." +- [Open Source Society University on Bioinformatics](https://github.com/ossu/bioinformatics) - Solid path for those of you who want to complete a Bioinformatics course on your own time, for free, with courses from the best universities in the World. +- [Rosalind](http://rosalind.info/) - Rosalind is a platform for learning bioinformatics through problem solving. +- [A guide for the lonely bioinformatician](http://www.opiniomics.org/a-guide-for-the-lonely-bioinformatician/) - This guide is aimed at bioinformaticians, and is meant to guide them towards better career development. - [A brief history of bioinformatics](https://doi.org/10.1093/bib/bby063) ### Bioinformatics on GitHub -- [Awesome-alternative-splicing](https://github.com/HussainAther/awesome-alternative-splicing) - 可变剪接资源列表，包括软件、数据库和其他工具. -- [Awesome AI-based Protein Design](https://github.com/opendilab/awesome-AI-based-protein-design) - 基于人工智能的蛋白质设计研究论文集. +- [Awesome-alternative-splicing](https://github.com/HussainAther/awesome-alternative-splicing) - List of resources on alternative splicing including software, databases, and other tools. +- [Awesome AI-based Protein Design](https://github.com/opendilab/awesome-AI-based-protein-design) - A collection of research papers for AI-based protein design. ### Sequencing -- [Next-Generation Sequencing Technologies - Elaine Mardis (2014)](https://youtu.be/6Is3W7JkFp8) [1:34:35] - 对下一代和第三代测序技术的出色（技术）概述，以及在癌症研究中的一些应用. -- [Annotated bibliography of \*Seq assays](https://liorpachter.wordpress.com/seq/) - 约 100 篇关于从转录到转座因子发现的各种测序技术和分析的论文列表. -- [For all you seq... (PDF)](http://www.illumina.com/content/dam/illumina-marketing/documents/applications/ngs-library-prep/ForAllYouSeqMethods.pdf) (3456x5471) - Illumina 的大量信息图，说明有多少种测序技术有效. 技术涵盖蛋白质-蛋白质相互作用、RNA 转录、RNA-蛋白质相互作用、RNA 低水平检测、RNA 修饰、RNA 结构、DNA 重排和标记、DNA 低水平检测、表观遗传学和 DNA-蛋白质相互作用. 包括参考资料. +- [Next-Generation Sequencing Technologies - Elaine Mardis (2014)](https://youtu.be/6Is3W7JkFp8) [1:34:35] - Excellent (technical) overview of next-generation and third-generation sequencing technologies, along with some applications in cancer research. +- [Annotated bibliography of \*Seq assays](https://liorpachter.wordpress.com/seq/) - List of ~100 papers on various sequencing technologies and assays ranging from transcription to transposable element discovery. +- [For all you seq... (PDF)](http://www.illumina.com/content/dam/illumina-marketing/documents/applications/ngs-library-prep/ForAllYouSeqMethods.pdf) (3456x5471) - Massive infographic by Illumina on illustrating how many sequencing techniques work. Techniques cover protein-protein interactions, RNA transcription, RNA-protein interactions, RNA low-level detection, RNA modifications, RNA structure, DNA rearrangements and markers, DNA low-level detection, epigenetics, and DNA-protein interactions. References included. ### RNA-Seq -- [Review papers on RNA-seq (Biostars)](https://www.biostars.org/p/52152/) - 包括许多关于 RNA-seq 和分析方法的开创性论文. -- [Informatics for RNA-seq: A web resource for analysis on the cloud](https://github.com/griffithlab/rnaseq_tutorial) - 关于使用 Amazon AWS 云服务在云中执行 RNA-seq 分析的教育资源. 主题包括准备数据、预处理、差异表达、异构体发现、数据可视化和解释. -- [RNA-seqlopedia](http://rnaseq.uoregon.edu/) - RNA-seqlopedia 对 RNA-seq 以及进行成功的 RNA-seq 实验所需的选择进行了精彩的概述. -- [A survey of best practices for RNA-seq data analysis](http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8) - 为 RNA-seq 计算分析提供了出色的路线图，包括挑战/障碍和需要注意的事项，以及如何将 RNA-seq 数据与其他数据类型集成. -- [Stories from the Supplement](https://www.youtube.com/watch?v=5NiFibnbE8o) [46:39] - Lior Pachter 博士分享了他在著名 RNA-seq 分析软件 CuffDiff 和 [Cufflinks](http://cole-trapnell-lab.github.io/cufflinks/) 并解释了他们的一些方法. -- [List of RNA-seq Bioinformatics Tools](https://en.wikipedia.org/wiki/List_of_RNA-Seq_bioinformatics_tools) - 维基百科上的分析所需的 RNA-seq 生物信息学工具的广泛列表，范围从质量控制、对齐、拼接分析和可视化的分析管道的所有部分. -- [RNA-seq Analysis](https://github.com/crazyhottommy/RNA-seq-analysis) - [@crazyhottommy](https://github.com/crazyhottommy)进行 RNA-seq 分析时关于各种步骤和注意事项的注释. +- [Review papers on RNA-seq (Biostars)](https://www.biostars.org/p/52152/) - Includes lots of seminal papers on RNA-seq and analysis methods. +- [Informatics for RNA-seq: A web resource for analysis on the cloud](https://github.com/griffithlab/rnaseq_tutorial) - Educational resource on performing RNA-seq analysis in the cloud using Amazon AWS cloud services. Topics include preparing the data, preprocessing, differential expression, isoform discovery, data visualization, and interpretation. +- [RNA-seqlopedia](http://rnaseq.uoregon.edu/) - RNA-seqlopedia provides an awesome overview of RNA-seq and of the choices necessary to carry out a successful RNA-seq experiment. +- [A survey of best practices for RNA-seq data analysis](http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8) - Gives awesome roadmap for RNA-seq computational analyses, including challenges/obstacles and things to look out for, but also how you might integrate RNA-seq data with other data types. +- [Stories from the Supplement](https://www.youtube.com/watch?v=5NiFibnbE8o) [46:39] - Dr. Lior Pachter shares his stories from the supplement for well-known RNA-seq analysis software CuffDiff and [Cufflinks](http://cole-trapnell-lab.github.io/cufflinks/) and explains some of their methodologies. +- [List of RNA-seq Bioinformatics Tools](https://en.wikipedia.org/wiki/List_of_RNA-Seq_bioinformatics_tools) - Extensive list on Wikipedia of RNA-seq bioinformatics tools needed in analysis, ranging from all parts of an analysis pipeline from quality control, alignment, splice analysis, and visualizations. +- [RNA-seq Analysis](https://github.com/crazyhottommy/RNA-seq-analysis) - [@crazyhottommy](https://github.com/crazyhottommy)'s notes on various steps and considerations when doing RNA-seq analysis. ### ChIP-Seq -- [ChIP-seq analysis notes from Tommy Tang](https://github.com/crazyhottommy/ChIP-seq-analysis) - ChIP-seq 数据资源，包括论文、方法、软件链接和分析. +- [ChIP-seq analysis notes from Tommy Tang](https://github.com/crazyhottommy/ChIP-seq-analysis) - Resources on ChIP-seq data which include papers, methods, links to software, and analysis. ### YouTube Channels and Playlists -- [Current Topics in Genome Analysis 2016](https://www.genome.gov/12514288/current-topics-in-genome-analysis-2016-course-syllabus-handouts-and-videos/) - 在 NIH 举办的 14 场精彩系列讲座，内容涉及基因组学的当前主题，从序列分析到测序技术，甚至更多的转化主题，如基因组医学. -- [GenomeTV](https://www.youtube.com/user/GenomeTV) - “GenomeTV 是 NHGRI 的官方视频资源集，从讲座到新闻纪录片，再到处理基因组研究的研究、问题和临床应用的会议的完整视频集.” -- [Leading Strand](https://www.youtube.com/user/LeadingStrand) - 冷泉港实验室 (CSHL) 会议的主题演讲. 更多关于 [The Leading Strand](http://theleadingstrand.cshl.edu/). -- [Genomics, Big Data and Medicine Seminar Series](https://www.youtube.com/playlist?list=PLqLDR0CTP9_pboZCk6gR9Zn4kW7h9XWJI) - “我们的研讨会致力于 GBM 的关键交叉点，深入研究将深刻塑造未来的‘前沿’技术和方法.” -- [Rafael Irizarry's Channel](https://www.youtube.com/user/RafalabChannel/videos) - Rafael Irizarry 博士关于基因组学统计的讲座和学术讲座. -- [NIH VideoCasting and Podcasting](https://www.youtube.com/user/nihvcast) - “NIH VideoCast 以实时流媒体视频的形式通过 Internet 向全球观众直播研讨会、大会和会议.” 不仅仅是基因组学和生物信息学视频，还有许多关于生物信息学和基因组学领域特定用途的精彩演讲. +- [Current Topics in Genome Analysis 2016](https://www.genome.gov/12514288/current-topics-in-genome-analysis-2016-course-syllabus-handouts-and-videos/) - Excellent series of fourteen lectures given at NIH about current topics in genomics ranging from sequence analysis, to sequencing technologies, and even more translational topics such as genomic medicine. +- [GenomeTV](https://www.youtube.com/user/GenomeTV) - "GenomeTV is NHGRI's collection of official video resources from lectures, to news documentaries, to full video collections of meetings that tackle the research, issues and clinical applications of genomic research." +- [Leading Strand](https://www.youtube.com/user/LeadingStrand) - Keynote lectures from Cold Spring Harbor Laboratory (CSHL) Meetings. More on [The Leading Strand](http://theleadingstrand.cshl.edu/). +- [Genomics, Big Data and Medicine Seminar Series](https://www.youtube.com/playlist?list=PLqLDR0CTP9_pboZCk6gR9Zn4kW7h9XWJI) - "Our seminars are dedicated to the critical intersection of GBM, delving into 'bleeding edge' technology and approaches that will deeply shape the future." +- [Rafael Irizarry's Channel](https://www.youtube.com/user/RafalabChannel/videos) - Dr. Rafael Irizarry's lectures and academic talks on statistics for genomics. +- [NIH VideoCasting and Podcasting](https://www.youtube.com/user/nihvcast) - "NIH VideoCast broadcasts seminars, conferences and meetings live to a world-wide audience over the Internet as a real-time streaming video." Not exclusively genomics and bioinformatics video but many great talks on domain specific use of bioinformatics and genomics. ### Blogs -- [ACGT](http://www.acgt.me/) - 基思·布拉德南 (Keith Bradnam) 博士写道：“关于生物学、基因组学的思考，以及生物信息学首字母缩略词的滥用对人类的持续威胁.” -- [Opiniomics](http://www.opiniomics.org/) - Mick Watson 博士撰写有关生物信息学、基因组和生物学的文章. -- [Bits of DNA](https://liorpachter.wordpress.com/) - Lior Pachter 博士撰写有关计算生物学的评论和评论. -- [it is NOT junk](http://www.michaeleisen.org/blog/) - Michael Eisen 博士撰写了“关于基因组、DNA、进化、开放科学、棒球和其他重要事物的博客” -- [#!/perl/bioinfo](https://bioinfoperl.blogspot.com) - EEAD-CSIC 的计算和结构生物学小组用西班牙语和英语撰写关于植物基因组学、计算和结构生物学问题的想法和代码. +- [ACGT](http://www.acgt.me/) - Dr. Keith Bradnam writes about this "thoughts on biology, genomics, and the ongoing threat to humanity from the bogus use of bioinformatics acroynums." +- [Opiniomics](http://www.opiniomics.org/) - Dr. Mick Watson write on bioinformatics, genomes, and biology. +- [Bits of DNA](https://liorpachter.wordpress.com/) - Dr. Lior Pachter writes review and commentary on computational biology. +- [it is NOT junk](http://www.michaeleisen.org/blog/) - Dr. Michael Eisen writes "a blog about genomes, DNA, evolution, open science, baseball and other important things" +- [#!/perl/bioinfo](https://bioinfoperl.blogspot.com) - The Computational and Structural Biology group at EEAD-CSIC writes, in Spanish and English, about ideas and code for plant genomics, computational and structural biology problems. ### Miscellaneous -- [The Leek group guide to genomics papers](https://github.com/jtleek/genomicspapers/) - 专业策划的基因组学论文，以加快基因组学、RNA-seq、统计（用于基因组学）、软件开发等方面的速度. -- [A New Online Computational Biology Curriculum](https://doi.org/10.1371/journal.pcbi.1003662) - “本文介绍了数百个免费视频课程的目录，这些课程可能对那些希望扩展其生物信息学和计算生物学知识的人感兴趣.这些课程以大学院系为蓝本分为 11 个学科领域，并附有评论和职业建议. “ -- [How Perl Saved the Human Genome Project](http://www.foo.be/docs/tpj/issues/vol1_2/tpj0102-0001.html) - Lincoln D. Stein 关于 Perl 编程语言在人类基因组计划中的重要性的轶事. -- [Educational Papers from Nature Biotechnology and PLoS Computational Biology](https://liacs.leidenuniv.nl/~hoogeboomhj/mcb/nature_primer.html) - 关于计算生物学和生物信息学中使用的各种方法的引物和简短教育文章的链接页面. -- [The PeerJ Bioinformatics Software Tools Collection](https://peerj.com/collections/45-bioinformatics-software/) - 由 Keith Crandall 和 Claus White 策划的工具集，旨在整理 PeerJ 中最有趣、创新和相关的生物信息学工具文章. +- [The Leek group guide to genomics papers](https://github.com/jtleek/genomicspapers/) - Expertly curated genomics papers to get up to speed on genomics, RNA-seq, statistics (used in genomics), software development, and more. +- [A New Online Computational Biology Curriculum](https://doi.org/10.1371/journal.pcbi.1003662) - "This article introduces a catalog of several hundred free video courses of potential interest to those wishing to expand their knowledge of bioinformatics and computational biology. The courses are organized into eleven subject areas modeled on university departments and are accompanied by commentary and career advice." +- [How Perl Saved the Human Genome Project](http://www.foo.be/docs/tpj/issues/vol1_2/tpj0102-0001.html) - An anecdote by Lincoln D. Stein on the importance of the Perl programming language in the Human Genome Project. +- [Educational Papers from Nature Biotechnology and PLoS Computational Biology](https://liacs.leidenuniv.nl/~hoogeboomhj/mcb/nature_primer.html) - Page of links to primers and short educational articles on various methods used in computational biology and bioinformatics. +- [The PeerJ Bioinformatics Software Tools Collection](https://peerj.com/collections/45-bioinformatics-software/) - Collection of tools curated by Keith Crandall and Claus White, aimed at collating the most interesting, innovative, and relevant bioinformatics tools articles in PeerJ. ## Online networking groups -- [Bioinformatics (on Discord)](https://discord.com/invite/3uxbPns) - 用于通用生物信息学的 Discord 服务器 -- [r-bioinformatics](https://www.reddit.com/r/bioinformatics/comments/7ndwm1/rbioinformatics_slack_channel_and_an_open_call/) - r/bioinformatics 的官方 Slack 工作区 ([send a direct message to apfejes on reddit](https://www.reddit.com/message/compose/?to=apfejes&subject=Request%20to%20join%20the%20r/bioinformatics%20Slack%20group&message=I%20would%20like%20to%20request%20to%20join%20the%20r/bioinformatics%20Slack%20group)) -- [BioinformaticsGRX](https://bioinformaticsgrx.es/) - 位于西班牙格拉纳达的生物信息学家社区 -- [Comunidad de Desarolladores de Software en Bioinformática](https://comunidadbioinfo.github.io/) - 一个以拉丁美洲为中心的生物信息学家社区 -- [COMBINE](https://combine.org.au/) - 奥地利生物信息学学生小组 +- [Bioinformatics (on Discord)](https://discord.com/invite/3uxbPns) - a Discord server for general bioinformatics +- [r-bioinformatics](https://www.reddit.com/r/bioinformatics/comments/7ndwm1/rbioinformatics_slack_channel_and_an_open_call/) - the official Slack workspace of r/bioinformatics ([send a direct message to apfejes on reddit](https://www.reddit.com/message/compose/?to=apfejes&subject=Request%20to%20join%20the%20r/bioinformatics%20Slack%20group&message=I%20would%20like%20to%20request%20to%20join%20the%20r/bioinformatics%20Slack%20group)) +- [BioinformaticsGRX](https://bioinformaticsgrx.es/) - A community of bioinformaticians based in Granada, Spain +- [Comunidad de Desarolladores de Software en Bioinformática](https://comunidadbioinfo.github.io/) - A community of bioinformaticians centered in Latin America +- [COMBINE](https://combine.org.au/) - An Austrialian group for bioinformatics students ## License diff --git a/docs/awesome/awesome-agi-cocosci.md b/docs/awesome/awesome-agi-cocosci.md index 11ed0237e1..4900a6670e 100644 --- a/docs/awesome/awesome-agi-cocosci.md +++ b/docs/awesome/awesome-agi-cocosci.md @@ -544,7 +544,7 @@ organized into intricate patterns that seem to be consistent across individuals. * [No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization](https://ieeexplore.ieee.org/abstract/document/9904438) - ***IEEE Transactions on Visualization and Computer Graphics***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=17206818917381447796)]. There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (such as ML). Despite the prominence of this interface form, there has been little detailed analysis of the characteristics of these languages. This study surveys and analyzes the design and implementation of 57 JSON-style DSLs for visualization. The authors analyze these languages supported by a collected corpus of examples for each DSL (consisting of 4395 instances) across a variety of axes organized into concerns related to domain, conceptual model, language relationships, affordances, and general practicalities. The authors identify tensions throughout these areas, such as between formal and colloquial specifications, among types of users, and within the composition of languages. Through this work, the authors seek to support language implementers by elucidating the choices, opportunities, and tradeoffs in visualization DSL design. -* [Quantifying usability of domain-specific languages: An empirical study on software maintenance](https://www.sciencedirect.com/science/article/pii/S0164121214002799) - ***Journal of Systems and Software***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=3450893039446010260)]. A study to compare the usability of textual DSLs under the perspective of software maintenance, suggesting that the proposed metrics were useful: (1) to early identify DSL usability limitations, (2) to reveal specific DSL features favoring maintenance tasks, and (3) to successfully analyze eight critical DSL usability dimensions. +* [Quantifying usability of domain-specific languages: An empirical study on software maintenance](https://www.sciencedirect.com/science/article/pii/S0164121214002799) - ***Journal of Systems and Software***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=3450893039446010260)]. A DSL aims to support software development by offering abstractions to a particular domain. It is expected that DSLs improve the maintainability of artifacts otherwise produced with general-purpose languages. However, the maintainability of the DSL artifacts and, hence, their adoption in mainstream development, is largely dependent on the usability of the language itself. Unfortunately, it is often hard to identify their usability strengths and weaknesses early, as there is no guidance on how to objectively reveal them. Usability is a multi-faceted quality characteristic, which is challenging to quantify beforehand by DSL stakeholders. There is even less support on how to quantitatively evaluate the usability of DSLs used in maintenance tasks. In this context, this paper reports a study to compare the usability of textual DSLs under the perspective of software maintenance. A usability measurement framework was developed based on the cognitive dimensions of notations. The framework was evaluated both qualitatively and quantitatively using two DSLs in the context of two evolving object-oriented systems. The results suggested that the proposed metrics were useful: (1) to early identify DSL usability limitations, (2) to reveal specific DSL features favoring maintenance tasks, and (3) to successfully analyze eight critical DSL usability dimensions. * [How Domain Experts Use an Embedded DSL](https://dl.acm.org/doi/abs/10.1145/3622851) - ***OOPSLA'23***, 2023. This work conducts a thematic analysis identified five key themes, including: the interaction between the eDSL and the host language has significant and sometimes unexpected impacts on eDSL user experience, and users preferentially engage with domain-specific communities and code templates rather than host language resources. diff --git a/docs/awesome/awesome-angular.md b/docs/awesome/awesome-angular.md index 69a720db84..66b3daac00 100644 --- a/docs/awesome/awesome-angular.md +++ b/docs/awesome/awesome-angular.md @@ -448,6 +448,7 @@ become an Angular expert. * [bolt](https://bolt.new/) - Dev sandbox with AI from StackBlitz. * [CodeSandbox](https://codesandbox.io/s/angular-angular) - An online IDE and prototyping tool for rapid Angular development. * [IDX](https://idx.dev/) - Project IDX is an AI-assisted workspace for full-stack, multiplatform app development in the cloud. +* [ngx-html-syntax](https://github.com/princemaple/ngx-html-syntax) - Angular HTML Syntax for [SublimeText](https://www.sublimetext.com/). * [Bugfender](https://bugfender.com/platforms/angular-logging/) - A cloud service to collect logs and Angular errors in Real-Time. * [Angular Dev Tools](https://angular.dev/tools/devtools) - Angular DevTools is a browser extension that provides debugging and profiling capabilities for Angular applications. * [VS Code NG Language Service](https://github.com/angular/vscode-ng-language-service) - This extension provides a rich editing experience for Angular templates. @@ -901,6 +902,7 @@ become an Angular expert. * [ngx-graph](https://github.com/swimlane/ngx-graph) - Graph visualization library for Angular. * [ng-draw-flow](https://github.com/taiga-family/ng-draw-flow) - A library for creating interfaces based on displaying data as nodes. It allows you to create both simple diagrams and complex editors for building customised charts and graphs. * [sequential-workflow-designer](https://github.com/nocode-js/sequential-workflow-designer) - Customizable no-code component for building flow-based programming applications or workflow automation. Zero external dependencies. +* [ngx-hierarchy](https://github.com/rushik1992/ngx-hierarchy) - Angular Component Module for Vertical or Horizontal Hierarchy/Tree View with flexible dynamic template design and controls. #### Cookies diff --git a/docs/awesome/awesome-ble.md b/docs/awesome/awesome-ble.md index 51ccb9976e..b5ccafbc06 100644 --- a/docs/awesome/awesome-ble.md +++ b/docs/awesome/awesome-ble.md @@ -116,6 +116,7 @@ _A list of resources dedicated to EspressIf hardware_ - [EspressIf](https://www.espressif.com/) - Official Website - [ESP-IDF](https://github.com/espressif/esp-idf) - Espressif IoT Development Framework +- [ESP-32-C3 + Rust + BLE](https://dev.to/theembeddedrustacean/series/26733) - ESP32 Bluetooth w/ Rust Series' Articles ## Nordic diff --git a/docs/awesome/awesome-chatgpt.md b/docs/awesome/awesome-chatgpt.md index 1082b21bc8..5d6ba0d9c0 100644 --- a/docs/awesome/awesome-chatgpt.md +++ b/docs/awesome/awesome-chatgpt.md @@ -253,6 +253,7 @@ - [Chapyter](https://github.com/chapyter/chapyter) - Integrate ChatGPT capabilities into Jupyter Notebook. - [I Don't Care About Commit Message](https://github.com/mefengl/vscode-i-dont-care-about-commit-message) - Auto-generate commit messages and push actions in VS Code. - [Python Smart Exceptions](https://github.com/roma-guru/smart-exceptions) - AI-powered Python exceptions debugging. +- [TextCraft](https://github.com/suncloudsmoon/TextCraft) - AI-powered Word add-in for text generation and proofreading. ## Packages diff --git a/docs/awesome/awesome-cpp.md b/docs/awesome/awesome-cpp.md index 2680086dd9..2ea1d0e02a 100644 --- a/docs/awesome/awesome-cpp.md +++ b/docs/awesome/awesome-cpp.md @@ -372,6 +372,7 @@ A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny * [sqlite_modern_cpp](https://github.com/SqliteModernCpp/sqlite_modern_cpp) - Header only C++14 wrapper around sqlite library. [MIT] * [sqlite_orm](https://github.com/fnc12/sqlite_orm) - SQLite ORM light header only library for modern C++. [AGPL + paid MIT] * [sqlpp11](https://github.com/rbock/sqlpp11) - A type safe embedded domain specific language for SQL queries and results in C++. [BSD-2-Clause] +* [TidesDB](https://github.com/tidesdb/tidesdb) - High-performance, durable, transactional embedded storage engine designed for flash and RAM optimization. [MPL-2.0] [website](https://tidesdb.com/) * [TileDB](https://github.com/TileDB-Inc/TileDB) - Fast Dense and Sparse Multidimensional Array DBMS. [MIT] [website](https://tiledb.io/) * [TinyORM](https://github.com/silverqx/TinyORM) - Modern C++ ORM library. [MIT] [website](https://www.tinyorm.org/) * [UnQLite](https://github.com/symisc/unqlite) - A self-contained, serverless, zero-configuration, transactional NoSQL engine. [BSD-2-Clause] [website](https://unqlite.org/) @@ -974,6 +975,7 @@ A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny * [djinni](https://djinni.xlcpp.dev) - A tool for generating cross-language type declarations and interface bindings. [Apache2] * [Duktape](https://github.com/svaarala/duktape) - An embeddable JavaScript engine with compact footprint. [MIT] [website](http://duktape.org) * [JavaCpp](https://github.com/bytedeco/javacpp) - The missing bridge between Java and native C++. [Apache2] +* [JerryScript](https://github.com/jerryscript-project/jerryscript) - Ultra-lightweight JavaScript engine for the Internet of Things. [Apache-2.0] [website](https://jerryscript.net/) * [libffi](https://github.com/libffi/libffi) - A portable foreign-function interface library. [MIT] [website](https://sourceware.org/libffi/) * [Lua](http://www.lua.org/) - A minimal and fast scripting engine for configuration files and basic application scripting. [MIT] * [LuaBridge](https://github.com/vinniefalco/LuaBridge) - A lightweight, dependency-free library for binding Lua to C++. [MIT] diff --git a/docs/awesome/awesome-css-frameworks.md b/docs/awesome/awesome-css-frameworks.md index 4b1ec14f89..1718525953 100644 --- a/docs/awesome/awesome-css-frameworks.md +++ b/docs/awesome/awesome-css-frameworks.md @@ -298,19 +298,6 @@ We are keeping them here for reference/inspiration and hope for a revival. [Repo](https://github.com/semantic-org/semantic-ui) | #LESS -- [**Materialize**](https://materializecss.com) - CSS framework based on material design. - ![](https://img.shields.io/github/stars/Dogfalo/materialize.svg?style=social&label=Star) - [Demo](https://materializecss.com/showcase.html), - [Docs](https://materializecss.com/getting-started.html), - [Repo](https://github.com/Dogfalo/materialize) - | #SCSS - -- [**Spectre.css**](https://picturepan2.github.io/spectre/) - Lightweight, responsive and modern CSS framework. - ![](https://img.shields.io/github/stars/picturepan2/spectre.svg?style=social&label=Star) - [Docs](https://picturepan2.github.io/spectre/getting-started.html), - [Repo](https://github.com/picturepan2/spectre) - | #SCSS - - [**Tachyons**](https://tachyons.io) - Quickly build and design new UI without writing CSS. ![](https://img.shields.io/github/stars/tachyons-css/tachyons.svg?style=social&label=Star) [Demo](https://tachyons.io/gallery/), @@ -318,33 +305,16 @@ We are keeping them here for reference/inspiration and hope for a revival. [Repo](https://github.com/tachyons-css/tachyons/) | #CSS -- [**Milligram**](https://milligram.io) - Minimalist CSS framework. - ![](https://img.shields.io/github/stars/milligram/milligram.svg?style=social&label=Star) - [Demo](https://milligram.io/showcase.html), - [Docs](https://milligram.io/#getting-started), - [Repo](https://github.com/milligram/milligram) - | #Sass - - [**Water.css**](https://watercss.kognise.dev/) - Just-add-CSS collection of styles to make simple websites just a little nicer. ![](https://img.shields.io/github/stars/kognise/water.css.svg?style=social&label=Star) [Repo](https://github.com/kognise/water.css) | #CSS -- [**Basscss**](https://basscss.com) - Low-level CSS toolkit. - ![](https://img.shields.io/github/stars/basscss/basscss.svg?style=social&label=Star) - [Repo](https://github.com/basscss/basscss/) - - [**sanitize.css**](https://csstools.github.io/sanitize.css/) - The best-practices CSS foundation. ![](https://img.shields.io/github/stars/csstools/sanitize.css.svg?style=social&label=Star) [Repo](https://github.com/csstools/sanitize.css) | #CSS -- [**MUI**](https://www.muicss.com) - Lightweight CSS framework that follows Google's Material Design guidelines. - ![](https://img.shields.io/github/stars/muicss/mui.svg?style=social&label=Star) - [Docs](https://www.muicss.com/docs/v1/getting-started/introduction), - [Repo](https://github.com/muicss/mui) - | #SCSS - - [**modern-css-reset**](https://github.com/hankchizljaw/modern-css-reset) - Tiny little reset that you can use as the basis of your CSS projects. ![](https://img.shields.io/github/stars/hankchizljaw/modern-css-reset.svg?style=social&label=Star) [Repo](https://github.com/hankchizljaw/modern-css-reset) @@ -364,22 +334,12 @@ We are keeping them here for reference/inspiration and hope for a revival. [Repo](https://github.com/inuitcss/inuitcss) | #SCSS -- [**Cutestrap**](https://www.cutestrap.com) - Sassy, opinionated CSS framework, a tiny alternative to Bootstrap. - ![](https://img.shields.io/github/stars/tylerchilds/cutestrap.svg?style=social&label=Star) - [Repo](https://github.com/tylerchilds/cutestrap) - - [**unsemantic**](https://unsemantic.com) - Fluid grid for mobile, tablet, and desktop. ![](https://img.shields.io/github/stars/nathansmith/unsemantic.svg?style=social&label=Star) [Demo](https://unsemantic.com/demo-responsive), [Docs](https://unsemantic.com/css-documentation), [Repo](https://github.com/nathansmith/unsemantic) -- [**Base**](https://getbase.org) - Rock solid, responsive HTML/CSS framework. - ![](https://img.shields.io/github/stars/getbase/base.svg?style=social&label=Star) - [Docs](https://github.com/getbase/base/blob/master/readme.md#documentation), - [Repo](https://github.com/getbase/base) - | #SCSS #LESS - - [**Propeller**](https://propeller.in) - Front-end responsive framework based on Google's Material Design standards & Bootstrap. ![](https://img.shields.io/github/stars/digicorp/propeller.svg?style=social&label=Star) [Docs](https://propeller.in/frameworks/open-source/get-started/), diff --git a/docs/awesome/awesome-db-tools.md b/docs/awesome/awesome-db-tools.md index 45d6a2f54f..f09d765437 100644 --- a/docs/awesome/awesome-db-tools.md +++ b/docs/awesome/awesome-db-tools.md @@ -21,6 +21,7 @@ For updates on `awesome-db-tools` and thoughts/news about databases/tools/SQL fo - [DataGrip](https://www.jetbrains.com/datagrip) - Cross-Platform IDE for Databases & SQL by JetBrains. - [DataStation](https://github.com/multiprocessio/datastation) - Easily query, script, and visualize data from every database, file, and API. - [DBeaver](https://github.com/dbeaver/dbeaver) - Free universal database manager and SQL client. +- [dbForge Edge](https://www.devart.com/dbforge/edge/) - Multidatabase solution for DB development, design, management, and administration of MySQL, MariaDB, SQL Server, Oracle, PostgreSQL databases, and various cloud services. - [dbForge Studio for MySQL](https://www.devart.com/dbforge/mysql/studio) - Universal IDE for MySQL and MariaDB database development, management, and administration. - [dbForge Studio for Oracle](https://www.devart.com/dbforge/oracle/studio) - Powerful IDE for Oracle management, administration, and development. - [dbForge Studio for PostgreSQL](https://www.devart.com/dbforge/postgresql/studio) - GUI tool for managing and developing databases and objects. @@ -29,6 +30,7 @@ For updates on `awesome-db-tools` and thoughts/news about databases/tools/SQL fo - [dbKoda](https://github.com/SouthbankSoftware/dbkoda) - Modern (JavaScript/Electron framework), open source IDE for MongoDB. It has features to support development, administration and performance tuning on MongoDB databases. - [IBExpert](http://www.ibexpert.net/ibe) - Comprehensive GUI tool for Firebird and InterBase. - [HeidiSQL](https://github.com/HeidiSQL/HeidiSQL) - A lightweight client for managing MySQL, MSSQL and PostgreSQL, written in Delphi. +- [Kangaroo](https://github.com/dbkangaroo/kangaroo) - A AI-powered SQL client and admin tool for popular databases(SQLite / MySQL / PostgreSQL / etc) on Windows / macOS / Linux, support table design, query, model, sync, export/import etc, focus on comfortable, fun and developer friendly. - [KeepTool](https://keeptool.com) - A professional suite of tools for Oracle Database developers, administrators and advanced application users. - [MySQL Workbench](https://www.mysql.com/products/workbench) - MySQL Workbench is a unified visual tool for database architects, developers, and DBAs. - [Navicat](https://www.navicat.com/en/products#navicat) - A database development tool that allows you to simultaneously connect to MySQL, MariaDB, SQL Server, Oracle, PostgreSQL, and SQLite databases from a single application. @@ -312,12 +314,14 @@ Low-code and no-code platforms for application building ### Formatters - [CodeBuff](https://github.com/antlr/codebuff) - Language-agnostic pretty-printing through machine learning. +- [JSQLFormatter](https://github.com/manticore-projects/jsqlformatter) - Open Source Java SQL Formatter for many RDBMS based on [JSqlParser](https://github.com/JSQLParser/JSqlParser). - [pgFormatter](https://github.com/darold/pgFormatter) - A PostgreSQL SQL syntax beautifier. - [Poor SQL](https://poorsql.com) - Instant free and open-source T-SQL formatting. - [SQL Formatter](https://github.com/zeroturnaround/sql-formatter) - JavaScript library for pretty-printing SQL queries. ### Games - [Lost at SQL](https://lost-at-sql.therobinlord.com) - A SQL learning game to help you pick up basic SQL skills - so that you can use queries to get information. +- [Querymon](https://codepip.com/games/querymon/) - Learn to use SQL queries on the Querydex, a database of monsters from common to legendary. - [Schemaverse](https://datalemur.com/blog/games-to-learn-sql#schemaverse) - A Space-based strategy game implemented entirely within a PostgreSQL database. - [SQL Island](https://sql-island.informatik.uni-kl.de) - After the survived plane crash, you will be stuck on SQL Island for the time being. By making progress in the game, you will find a way to escape from this island. - [SQL Murder Mystery](https://mystery.knightlab.com) - Designed to be both a self-directed lesson to learn SQL concepts and commands and a fun game for experienced SQL users to solve an intriguing crime. diff --git a/docs/awesome/awesome-opentofu.md b/docs/awesome/awesome-opentofu.md index ec1ef61f6f..a1e5220bba 100644 --- a/docs/awesome/awesome-opentofu.md +++ b/docs/awesome/awesome-opentofu.md @@ -67,7 +67,7 @@ ### CI - [Atlantis](https://www.runatlantis.io/) - Automating workflows via pull requests. -- [TF-via-PR](https://github.com/DevSecTop/TF-via-PR) - GitHub Action to init, plan and apply Terraform/OpenTofu via PR automation. +- [TF-via-PR](https://github.com/OP5dev/TF-via-PR) - GitHub Action to init, plan and apply Terraform/OpenTofu via PR automation. - [pre-commit-opentofu](https://github.com/tofuutils/pre-commit-opentofu) - Git pre-commit hooks plugin. - [setup-opentofu](https://github.com/opentofu/setup-opentofu) - Set up OpenTofu CLI in your GitHub Actions workflow. - [terraform-github-actions](https://github.com/dflook/terraform-github-actions) - GitHub Actions for OpenTofu. diff --git a/docs/awesome/awesome-r-learning-resources.md b/docs/awesome/awesome-r-learning-resources.md index 9745726a37..a9835529f6 100644 --- a/docs/awesome/awesome-r-learning-resources.md +++ b/docs/awesome/awesome-r-learning-resources.md @@ -9,14 +9,14 @@
-> “Awesome R Learning Resources”存储库旨在帮助各种技能水平和背景的用户加深对“R”的理解，R 是一种用于统计计算和图形的编程语言和环境. +> The `Awesome R Learning Resources` repository is meant to help users from all skill levels and backgrounds deepen their understanding of `R`, which is a programming language and environment for statistical computing and graphics.
- > `R` `Discord` 服务器是一个面向 `R` 爱好者、程序员、统计学家、数据科学家和学生的友好而专注的社区. 无论您是想与其他用户联系，想要分享很棒的数据，或者只是需要帮助来完成您的统计任务，您都来对地方了！ +> The `R` `Discord` server is a friendly and dedicated community for `R` enthusiasts, programmers, statisticians, data scientists, and students. Whether you are looking to connect with fellow useRs, have awesome data viz to share, or just needed help with your stats assignment, you are at the right place!
- 要加入 R Discord 服务器，请单击下面的 discoRd 徽章.
+ To join the R Discord server, please click the discoRd badge below.
Discord
@@ -28,20 +28,20 @@ ### Comprehensive R Tutorials -- [Data Flair](https://data-flair.training/blogs/r-tutorials-home/) - 教程按技能水平（初级、中级、专家）分组. -- [Intro to R course by Fabio Votta - part 1](https://colab.research.google.com/drive/1dLsdGbkvgn1JbWgsy9Z-pFmPd_2MG4Xu?usp=sharing#scrollTo=vGnW7giO9AeD) - 对按类别（运算符、对象、函数、练习和数据框）分组的 R 编程的有趣介绍. -- [Intro to R course by Fabio Votta - part 2](https://colab.research.google.com/drive/14CRElnKewnp5MnlxhqVu6OOcIXd-Bkaj?usp=sharing) - 对按类别分组的 R 编程的有趣介绍（以看门人、tidyr 和 dplyr 包为特色的数据操作和清理）. -- [Introduction to Data Analysis with R](https://jmbuhr.de/dataIntro20/) - 这是一个包含介绍 R 和 tidyverse 以及统计概念的视频、脚本和练习的系列讲座. -- [R CODER](https://r-coder.com) - 教程分为几类（介绍、数据结构、数据整理、编程、导入和导出、图形），深入涵盖了开始学习 R 编程语言的人的所有基本需求. -- [Tutorials Point](https://www.tutorialspoint.com/r/index.htm) - 教程分为几类（R 教程、R 数据接口、R 图表和图形、R 统计示例、R 有用资源），深入涵盖了开始学习 R 编程语言的人的所有基本需求. +- [Data Flair](https://data-flair.training/blogs/r-tutorials-home/) - The tutorials are grouped by skill level (beginner, intermediate, expert). +- [Intro to R course by Fabio Votta - part 1](https://colab.research.google.com/drive/1dLsdGbkvgn1JbWgsy9Z-pFmPd_2MG4Xu?usp=sharing#scrollTo=vGnW7giO9AeD) - A fun introduction to R programming grouped into categories (operators, objects, functions, exercises, and data frames). +- [Intro to R course by Fabio Votta - part 2](https://colab.research.google.com/drive/14CRElnKewnp5MnlxhqVu6OOcIXd-Bkaj?usp=sharing) - A fun introduction to R programming grouped into categories (data manipulation and cleaning featuring the janitor, tidyr, and dplyr packages). +- [Introduction to Data Analysis with R](https://jmbuhr.de/dataIntro20/) - This is a lecture series with videos, scripts and exercises introducing R and the tidyverse as well as statistical concepts. +- [R CODER](https://r-coder.com) - The tutorials are grouped into categories (introduction, data structures, data wrangling, programming, import & export, graphics) that cover in-depth all the basic needs for someone starting learning the R programming language. +- [Tutorials Point](https://www.tutorialspoint.com/r/index.htm) - The tutorials are grouped into categories (R tutorial, R Data Interfaces, R Charts & Graphs, R Statistics Examples, R Useful Resources) that cover in-depth all the basic needs for someone starting learning the R programming language. ### Functions -- [stat.berkeley - Introduction to Functions](https://www.stat.berkeley.edu/~statcur/Workshop2/Presentations/functions.pdf) - Integrating Computing into the Statistics Curricula（加州大学伯克利分校）的组织者对 R 语言函数的介绍. +- [stat.berkeley - Introduction to Functions](https://www.stat.berkeley.edu/~statcur/Workshop2/Presentations/functions.pdf) - An introduction to functions in the R language by the organizers of Integrating Computing into the Statistics Curricula (U.C. Berkeley). ### Generative Art -- [12 Months of aRt](https://www.williamrchase.com/work/art/) - 在 2019 年，William Chase 开始了一个项目，每月制作一系列完全使用 R 的新艺术品.在这个项目中，他探索了不同的技术，开发了算法，并提供了详细介绍每个月开发过程的帖子. +- [12 Months of aRt](https://www.williamrchase.com/work/art/) - In 2019, William Chase began a project to make a new series of artwork every month made entirely with R. In this project, he explored different techniques, developed algorithms, and provided detailed posts detailing the development process for each month. ### Joining Data @@ -49,172 +49,173 @@ ### Math -- [Descriptive Statistics](https://rcompanion.org/handbook/C_02.html) - 描述性统计教程，用于以提供对数据中包含的信息的洞察力的方式汇总数据. 作者：Salvatore S. Mangiafico. -- [Descriptive statistics in R](https://statsandr.com/blog/descriptive-statistics-in-r/) - 本文解释了如何计算 R 中的主要描述性统计数据以及如何以图形方式呈现它们. 作者 - 安托万苏特威. -- [Essential Math for Data Science](https://medium.com/s/story/essential-math-for-data-science-why-and-how-e88271367fbd) - 一篇讨论成为更好的数据科学家需要掌握的关键数学主题的文章. 作者：Tirthajyoti Sarkar. -- [Gallery of Statistical Distributions](https://www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm) - 作者：NIST/SEMATECH. -- [Plotting distributions (ggplot2)](http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2) /) - 绘制数据分布的教程. 作者：温斯顿张. +- [Descriptive Statistics](https://rcompanion.org/handbook/C_02.html) - A tutorial of descriptive statistics which are used to summarize data in a way that provides insight into the information contained in the data. Author: Salvatore S. Mangiafico. +- [Descriptive statistics in R](https://statsandr.com/blog/descriptive-statistics-in-r/) - This article explains how to compute the main descriptive statistics in R and how to present them graphically. Author - Antoine Soetewey. +- [Essential Math for Data Science](https://medium.com/s/story/essential-math-for-data-science-why-and-how-e88271367fbd) - An article discussing the key mathematical topics to master to become a better data scientist. Author: Tirthajyoti Sarkar. +- [Gallery of Statistical Distributions](https://www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm) - Author: NIST/SEMATECH. +- [Plotting distributions (ggplot2)](http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/) - A tutorial for plotting a distribution of data. Author: Winston Chang. ### Shiny -- [Awesome R Shiny](https://github.com/grabear/awesome-rshiny) - R Shiny 的精选资源列表. 作者：罗伯·吉尔摩. -- [Building Shiny Applications with R Tutorial (Deprecated)](https://rstudio.github.io/shiny/tutorial/#) - Shiny 入门教程. 请注意，本教程已弃用. 作者：RStudio. -- [Building Shiny apps - an interactive tutorial](https://deanattali.com/blog/building-shiny-apps-tutorial/) - 本教程是对一组 [presentation slides](https://docs.google.com/presentation/d/1dXhqqsD7dPOOdcC5Y7RW--dEU7UfU52qlb0YD3kKeLw/edit) 用于学习如何构建闪亮的应用程序. 作者：院长阿塔利. -- [How to Start with Shiny](https://vimeo.com/rstudioinc/review/131218530/212d8a5a7a) - 详细的入门视频教程. 作者：加勒特·格罗莱蒙德. -- [Learn Shiny](https://shiny.rstudio.com/tutorial/) - 此页面上的视频和书面教程主要是为不熟悉 Shiny 并需要指导介绍的用户设计的. 作者：RStudio. -- [Shiny Articles](https://shiny.rstudio.com/articles/) - 各种文章涵盖更高级的各个 Shiny 主题. 作者：RStudio. +- [Awesome R Shiny](https://github.com/grabear/awesome-rshiny) - A curated list of resources for R Shiny. Author: Rob Gilmore. +- [Building Shiny Applications with R Tutorial (Deprecated)](https://rstudio.github.io/shiny/tutorial/#) - Introductory tutorial to Shiny. Note, this tutorial is deprecated. Author: RStudio. +- [Building Shiny apps - an interactive tutorial](https://deanattali.com/blog/building-shiny-apps-tutorial/) - This tutorial is a hands-on activity complement to a set of [presentation slides](https://docs.google.com/presentation/d/1dXhqqsD7dPOOdcC5Y7RW--dEU7UfU52qlb0YD3kKeLw/edit) for learning how to build Shiny apps. Author: Dean Attali. +- [How to Start with Shiny](https://vimeo.com/rstudioinc/review/131218530/212d8a5a7a) - Detailed introductory video tutorial. Author: Garrett Grolemund. +- [Learn Shiny](https://shiny.rstudio.com/tutorial/) - The video and written tutorials on this page are primarily designed for users who are new to Shiny and want a guided introduction. Author: RStudio. +- [Shiny Articles](https://shiny.rstudio.com/articles/) - Various articles covering individual Shiny topics at a more advanced level. Author: RStudio. ### Spatial -- [An Introduction to Choropleth maps in R](https://rstudio-pubs-static.s3.amazonaws.com/324400_69a673183ba449e9af4011b1eeb456b9.html) - 作者：Henry Cann. -- [Getting latitude & longitude for any address](https://discourse.looker.com/t/get-latitude-longitude-for-any-location-through-google-sheets-and-plot-these-in-looker/5402) ——作者：布莱希特·维梅尔. -- [Map Plots Created With R And Ggmap](https://www.littlemissdata.com/blog/maps) - 作者：劳拉埃利斯. -- [Plot Spatial Data / Shapefiles in R](https://www.youtube.com/watch?v=uZtto0cYjZM) - 来自“数学等”YouTube 频道. +- [An Introduction to Choropleth maps in R](https://rstudio-pubs-static.s3.amazonaws.com/324400_69a673183ba449e9af4011b1eeb456b9.html) - Author: Henry Cann. +- [Getting latitude & longitude for any address](https://discourse.looker.com/t/get-latitude-longitude-for-any-location-through-google-sheets-and-plot-these-in-looker/5402) - Author: Brecht Vermeire. +- [Map Plots Created With R And Ggmap](https://www.littlemissdata.com/blog/maps) - Author: Laura Ellis. +- [Plot Spatial Data / Shapefiles in R](https://www.youtube.com/watch?v=uZtto0cYjZM) - From the "math et al" YouTube channel. ### Viz -- [A ggplot2 Tutorial for Beautiful Plotting in R](https://cedricscherer.netlify.app/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/) - 一个全面且易于遵循的教程，涵盖使用轴、标题、图例、背景、网格线、边距、多面板图、颜色、主题、线条、文本、坐标、图表类型、色带、平滑和交互式图. 作者：Cédric Sc​​herer. -- [Awesome ggplot2](https://github.com/erikgahner/awesome-ggplot2) - 很棒的 ggplot2 教程、包等的精选列表.作者：Erik Gahner Larsen. +- [A ggplot2 Tutorial for Beautiful Plotting in R](https://cedricscherer.netlify.app/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/) - A comprehensive and easy to follow tutorial that covers working with axes, titles, legends, backgrounds, grid lines, margins, multi-panel plots, colors, themes, lines, text, coordinates, chart types, ribbons, smoothings, and interactive plots. Author: Cédric Scherer. +- [AISEKA](https://www.aiseka.com/) - Discover the best Color Palette & Color Tools. Author: meetqy. +- [Awesome ggplot2](https://github.com/erikgahner/awesome-ggplot2) - A curated list of awesome ggplot2 tutorials, packages etc. Author: Erik Gahner Larsen. - [Chart Suggestions — A thought-starter on choosing the way to show your data](https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf) - Author: Andrew Abela, Ph.D. -- [Color Hex Color Codes](https://www.color-hex.com/) - 作者：Color-Hex. -- [Combine Multiple GGPlots into a Figure](https://www.datanovia.com/en/lessons/combine-multiple-ggplots-into-a-figure/) - 作者：Alboukadel Kassambara. -- [Coolors](https://coolors.co/) - 超快的配色方案生成器！ 创建完美的调色板或从数千种美丽的配色方案中汲取灵感. 功能包括颜色选择器、从照片中选择调色板、创建拼贴画、制作您自己的渐变调色板、创建渐变、对比度检查器等. -- [From Data to Viz](https://www.data-to-viz.com/) - 从数据到可视化引导您找到最适合您的数据的图表. 作者：严霍尔茨. -- [ggplot2 extensions - gallery](https://exts.ggplot2.tidyverse.org/gallery/) - 由 Daniel Emaasit 维护. -- [ggplot2 - Modify components of a theme](https://ggplot2.tidyverse.org/reference/theme.html) - 如何在 ggplot2 中修改主题的组件. 作者：Tidyverse 的开发者. -- [Graphics in R with ggplot2](https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/) - 在 ggplot2 中使用图形的详细指南. 作者：安托万·索特威. -- [htmlwidgets for R](https://www.htmlwidgets.org/) - 您可以使用 R 构建的各种交互式 Web 可视化的展示和画廊. -- [r-color-palettes](https://github.com/EmilHvitfeldt/r-color-palettes) - r 中可用的调色板综合列表. 作者：Emil Hvitfeldt. -- [The Data Visualization Catalogue](https://datavizcatalogue.com/index.html) - 数据可视化目录是由 Severino Ribecca 开发的一个项目，用于创建不同信息可视化类型的库. -- [The Graphic Continuum](https://www.informationisbeautifulawards.com/showcase/611-the-graphic-continuum) - 图形连续体显示了我们在编码和呈现数据时可用的许多不同类型的可视化. 作者：Jonathan Schwabish 和 Severino Ribecca. -- [The R Graph Gallery](https://www.r-graph-gallery.com/) - 用 R 编程语言制作的图表集. 作者：严霍尔茨. -- [Time Based Heatmaps in R](https://www.littlemissdata.com/blog/heatmaps) - 作者：劳拉埃利斯. -- [Top 50 ggplot2 Visualizations - The Master List (With Full R Code)](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html) - 本教程帮助您为您的特定目标选择正确的图表类型，以及如何使用 ggplot2 在 R 中实现它. 作者：塞尔瓦·普拉巴卡兰. +- [Color Hex Color Codes](https://www.color-hex.com/) - Author: Color-Hex. +- [Combine Multiple GGPlots into a Figure](https://www.datanovia.com/en/lessons/combine-multiple-ggplots-into-a-figure/) - Author: Alboukadel Kassambara. +- [Coolors](https://coolors.co/) - The super fast color schemes generator! Create the perfect palette or get inspired by thousands of beautiful color schemes. Features include color picker, pick palette from photo, create a collage, make your own gradient palette, create a gradient, contrast checker, etc. +- [From Data to Viz](https://www.data-to-viz.com/) - From Data to Viz leads you to the most appropriate graph for your data. Author: Yan Holtz. +- [ggplot2 extensions - gallery](https://exts.ggplot2.tidyverse.org/gallery/) - Maintained by Daniel Emaasit. +- [ggplot2 - Modify components of a theme](https://ggplot2.tidyverse.org/reference/theme.html) - How to modify components of a theme in ggplot2. Author: the developers of Tidyverse. +- [Graphics in R with ggplot2](https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/) - A detailed guide for the use of graphics within ggplot2. Author: Antoine Soetewey. +- [htmlwidgets for R](https://www.htmlwidgets.org/) - Showcase and gallery of the various interactive web visualizations you can build using R. +- [r-color-palettes](https://github.com/EmilHvitfeldt/r-color-palettes) - Comprehensive list of color palettes available in r. Author: Emil Hvitfeldt. +- [The Data Visualization Catalogue](https://datavizcatalogue.com/index.html) - The Data Visualization Catalogue is a project developed by Severino Ribecca to create a library of different information visualization types. +- [The Graphic Continuum](https://www.informationisbeautifulawards.com/showcase/611-the-graphic-continuum) - The Graphic Continuum shows the many different types of visualizations available to us when we encode and present data. Authors: Jonathan Schwabish, and Severino Ribecca. +- [The R Graph Gallery](https://www.r-graph-gallery.com/) - A collection of charts made with the R programming language. Author: Yan Holtz. +- [Time Based Heatmaps in R](https://www.littlemissdata.com/blog/heatmaps) - Author: Laura Ellis. +- [Top 50 ggplot2 Visualizations - The Master List (With Full R Code)](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html) - This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. Author: Selva Prabhakaran. ### Web Scraping -- [Web Scraping Reference: Cheat Sheet for Web Scraping using R](https://github.com/yusuzech/r-web-scraping-cheat-sheet) - 使用 rvest、httr 和 Rselenium 进行网络抓取的指南、参考和备忘单. 作者： [yifyan et al.](https://github.com/yusuzech) +- [Web Scraping Reference: Cheat Sheet for Web Scraping using R](https://github.com/yusuzech/r-web-scraping-cheat-sheet) - Guide, reference and cheatsheet on web scraping using rvest, httr and Rselenium. Author: [yifyan et al.](https://github.com/yusuzech) ### Wrangling -- [Data Wrangling Part 1: Basic to Advanced Ways to Select Columns](https://suzan.rbind.io/2018/01/dplyr-tutorial-1/) - 作者：苏珊·贝尔特. -- [Data Wrangling Part 2: Transforming your columns into the right shape](https://suzan.rbind.io/2018/02/dplyr-tutorial-2/) - 作者：苏珊·贝尔特. -- [Data Wrangling Part 3: Basic and more advanced ways to filter rows](https://suzan.rbind.io/2018/02/dplyr-tutorial-3/) - 作者：苏珊·贝尔特. -- [Data Wrangling Part 4: Summarizing and slicing your data](https://suzan.rbind.io/2018/04/dplyr-tutorial-4/) - 作者：苏珊·贝尔特. +- [Data Wrangling Part 1: Basic to Advanced Ways to Select Columns](https://suzan.rbind.io/2018/01/dplyr-tutorial-1/) - Author: Suzan Baert. +- [Data Wrangling Part 2: Transforming your columns into the right shape](https://suzan.rbind.io/2018/02/dplyr-tutorial-2/) - Author: Suzan Baert. +- [Data Wrangling Part 3: Basic and more advanced ways to filter rows](https://suzan.rbind.io/2018/02/dplyr-tutorial-3/) - Author: Suzan Baert. +- [Data Wrangling Part 4: Summarizing and slicing your data](https://suzan.rbind.io/2018/04/dplyr-tutorial-4/) - Author: Suzan Baert. ### Uncategorized -- [Data.Table and Dplyr Tour](https://atrebas.github.io/post/2019-03-03-datatable-dplyr/#reshape-data) - R 包 data.table 和 dplyr 的详细比较. 作者：阿特巴斯. -- [data.table: A gentle introduction](https://atrebas.github.io/post/2020-06-17-datatable-introduction/) - 快速介绍 data.table. 主要目标是介绍 data.table 语法，展示如何执行基本但必不可少的数据整理任务. 作者：阿特巴斯. -- [Fakir - Create Fake Data in R for Tutorials](https://thinkr-open.github.io/fakir/) - 作者：科林费伊. -- [From base R to stringr](https://stringr.tidyverse.org/articles/from-base.html) - 此小插图将 stringr 函数与其基本 R 等效函数进行比较，以帮助用户从使用基本 R 过渡到使用 stringr. 作者：萨拉·斯托特. -- [Help me help you: creating reproducible examples](https://www.youtube.com/watch?v=5gqksthQ0cM) - 制作出色的代表既是一门艺术也是一门科学，本次网络研讨会将涵盖这两个方面. 代表使有关代码的对话对所有人来说都更加高效和愉快. 每当您向某人寻求帮助、报告软件错误或提出新功能时，它就会出现. reprex 包 (https://reprex.Tidyverse.org) 使得将 R 代码准备为 reprex 特别容易，以便在 https://community.rstudio.com、https://github 等网站上共享. com，或 https://stackoverflow.com. 作者：珍妮布莱恩. -- [R - discoRd server](https://discord.gg/88uG5UVyE2) - 具有以下基于主题的频道的专用 discoRd 服务器：用于更一般性讨论的“R-Main”，用于展示数据视觉效果的“R-Share”，用于提问和共享学习资源的“General R Help”，以及“Topical” Help/Discussion` 用于处理统计、dbi、tidymodels、shiny、自然科学、社会科学、贝叶斯、gis 和金融的问题. -- [Subreddit - r/Rlanguage - R Programming Language](https://www.reddit.com/r/Rlanguage/new/) - 一个 Reddit subreddit，专注于为统计和数据科学实施 R 编程语言. -- [Subreddit - r/programming - The R Project for Statistical Computing](https://www.reddit.com/r/rprogramming/) - 一个专注于使用 R 进行统计计算的 Reddit subreddit. -- [Syntax equivalents: base R vs Tidyverse](https://tavareshugo.github.io/data_carpentry_extras/base-r_tidyverse_equivalents/base-r_tidyverse_equivalents.html) - base R 和 tidyverse 的详细比较. 作者：雨果·塔瓦雷斯. -- [The ultimate R data.table cheat sheet](https://www.infoworld.com/article/3575086/the-ultimate-r-datatable-cheat-sheet.html) - 在这个可搜索的 R data.table 备忘单和 Tidyverse 代码中查找许多数据任务的代码. 作者：沙龙马奇利斯. +- [Data.Table and Dplyr Tour](https://atrebas.github.io/post/2019-03-03-datatable-dplyr/#reshape-data) - A detailed comparison of R packages data.table and dplyr. Author: Atrebas. +- [data.table: A gentle introduction](https://atrebas.github.io/post/2020-06-17-datatable-introduction/) - A quick introduction to data.table. The main objective is to present the data.table syntax, showing how to perform basic, but essential, data wrangling tasks. Author: Atrebas. +- [Fakir - Create Fake Data in R for Tutorials](https://thinkr-open.github.io/fakir/) - Author: Colin Fay. +- [From base R to stringr](https://stringr.tidyverse.org/articles/from-base.html) - This vignette compares stringr functions to their base R equivalents to help users transitioning from using base R to stringr. Author: Sara Stoudt. +- [Help me help you: creating reproducible examples](https://www.youtube.com/watch?v=5gqksthQ0cM) - Making a great reprex is both an art and a science and this webinar will cover both aspects. A reprex makes a conversation about code more efficient and pleasant for all. This comes up whenever you ask someone for help, report a bug in software, or propose a new feature. The reprex package (https://reprex.Tidyverse.org) makes it especially easy to prepare R code as a reprex, in order to share on sites such as https://community.rstudio.com, https://github.com, or https://stackoverflow.com. Author: Jenny Bryan. +- [R - discoRd server](https://discord.gg/88uG5UVyE2) - Dedicated discoRd server with the following topic-based channels: `R-Main` for more general discussions, `R-Share` for showing off your data visuals, `General R Help` for asking questions and sharing learning resources, and `Topical Help/Discussion` for issues dealing with statistics, dbi, tidymodels, shiny, natural-science, social-science, bayesians, gis, and finance. +- [Subreddit - r/Rlanguage - R Programming Language](https://www.reddit.com/r/Rlanguage/new/) - A Reddit subreddit focused on implementing the R programming language for statistics and data science. +- [Subreddit - r/programming - The R Project for Statistical Computing](https://www.reddit.com/r/rprogramming/) - A Reddit subreddit focused on using R for statistical computing. +- [Syntax equivalents: base R vs Tidyverse](https://tavareshugo.github.io/data_carpentry_extras/base-r_tidyverse_equivalents/base-r_tidyverse_equivalents.html) - A detailed comparison of base R and tidyverse. Author: Hugo Tavares. +- [The ultimate R data.table cheat sheet](https://www.infoworld.com/article/3575086/the-ultimate-r-datatable-cheat-sheet.html) - Find code for dozens of data tasks in this searchable cheat sheet of R data.table and Tidyverse code. Author: Sharon Machlis. ## Blogs -- [Alex Cookson](https://www.alexcookson.com/) - Alex Cookson 喜欢制作精美的可视化效果和易于阅读的 R 概念演练. 他对有关媒体的数据特别感兴趣，例如书籍、电影和音乐剧. -- [Avery Robbins](https://www.avery-robbins.com) - Avery Robbins 喜欢学习和分享对他个人有益的有用或令人敬畏的事物. 这个网站是他积极做到这一点的工具：分享有用的知识、想法和技巧. -- [Tony ElHabr](https://tonyelhabr.rbind.io/) - Tony ElHabr 主要热衷于能源市场和体育分析. 他的博客提供了详细的教程、项目解释和演示. -- [Cédric Scherer](https://cedricscherer.netlify.app/) - Cédric Sc​​herer 是一位毕业的计算生态学家和自由数据可视化专家，他创建了跨所有学科、目的和风格的可视化，并定期教授数据可视化原理、R 和 ggplot2. -- [Data Imaginist](https://www.data-imaginist.com/) - Thomas Lin Pedersen 是一名数据科学家转为软件工程师，他专注于改善研究人员与他们生成的数据的交互. -- [Data meets Narrative](http://www.rebeccabarter.com/blog/) - Rebecca Barter 喜欢理解复杂、凌乱、有时甚至是荒谬的数据集，例如电子健康记录和保险索赔. 她的双重爱好是用通俗易懂的英语向其他人解释“看似复杂”的概念，以及探索和揭开复杂数据集背后的故事. -- [HighlandR](https://johnmackintosh.net/) - 约翰·麦金托什 (John Mackintosh) 的博客是他展示演示或研讨会、他在工作中学到的笔记、图表改造以及他目前没有在他的角色中使用的技巧和技术的地方. -- [Julia Silge](https://juliasilge.com/blog/) - Julia Silge 是 RStudio 的一名数据科学家和软件工程师，她从事开源建模工具方面的工作. 她热衷于制作漂亮的图表、统计编程语言 R、Jane Austen、黑咖啡和红酒. -- [Musings on R](https://martinctc.github.io/blog/) - Martin Chan 关于 R 和数据科学的所有内容的博客. 涵盖的主题包括比较 dplyr 和 data.table、Shiny 应用程序、ggplot、数据清理、使用 RStudio、采访其他 R 用户/数据科学家以及网络抓取. -- [rweekly](https://rweekly.org/about) - 来自整个 R 社区的每周更新，由 Bruce Zhao、Colin Fay、Eric Nantz、Hao Zhu、Jon Calder、Jonathan Carroll、Maelle Salmon、Ryo Nakagawara 和 Wolfram Qin 撰写. -- [r-bloggers](https://www.r-bloggers.com/) - R-Bloggers.com 由 Tal Galili 创建，是一个博客聚合器，汇集了撰写有关 R（英文）的博主所贡献的内容. 该网站帮助 R 博主和用户连接和关注 R 博客圈. -- [Ryo Nakagawara](https://ryo-n7.github.io/) - Ryo Nakagawara 是一名数据科学家，一直从事 R 和 SQL 方面的报告分析师和软件开发人员的工作，以改进 ACDI 和 VOCA 数据管道，创建 R 包、可重现的报告、仪表板和闪亮的应用程序来传达他的项目如何全世界都在进步. -- [Statistics Globe](https://statisticsglobe.com/) - Joachim Schork 建立这个平台是为了分享他的统计知识，并通过与其他统计学家和程序员的讨论来提高自己的统计技能. -- [Stats and R](https://www.statsandr.com/) - 通过他的博客，Antoine Soetewey（统计学博士）旨在帮助从事数据工作的学者和专业人士掌握重要的统计概念，并展示如何在 R 中应用它们. +- [Alex Cookson](https://www.alexcookson.com/) - Alex Cookson loves making beautiful visualizations and easy-to-read walkthroughs of R concepts. He's particularly interested in data about media, like books, movies, and musicals. +- [Avery Robbins](https://www.avery-robbins.com) - Avery Robbins loves to learn and to share useful or awesome things that have benefited him personally. This website is a tool for him to actively do just that: share knowledge, ideas, and tips that are helpful. +- [Tony ElHabr](https://tonyelhabr.rbind.io/) - Tony ElHabr is passionate mostly about energy markets and sports analytics. His blog provides detailed tutorials, project explanations, and presentations. +- [Cédric Scherer](https://cedricscherer.netlify.app/) - Cédric Scherer is a graduated computational ecologist and freelance data visualization expert who has created visualizations across all disciplines, purposes, and styles and regularly teaches data visualization principles, R, and ggplot2. +- [Data Imaginist](https://www.data-imaginist.com/) - Thomas Lin Pedersen is a data scientist turned software engineer who focuses on improving researchers’ interactions with the data they produce. +- [Data meets Narrative](http://www.rebeccabarter.com/blog/) - Rebecca Barter enjoys making sense of complex, messy and sometimes nonsensical datasets, such as electronic health records, and insurance claims. Her dual passions are explaining “seemingly complicated” concepts to others in plain English, and exploring and uncovering the stories that underlie complex datasets. +- [HighlandR](https://johnmackintosh.net/) - John Mackintosh's blog is a place for him to showcase demonstrations or workshops, notes he's learned at work, chart makeovers, and techniques and technology that he doesn't currently use in his role. +- [Julia Silge](https://juliasilge.com/blog/) - Julia Silge is a data scientist and software engineer at RStudio where she work on open source modeling tools. She is passionate about making beautiful charts, the statistical programming language R, Jane Austen, black coffee, and red wine. +- [Musings on R](https://martinctc.github.io/blog/) - A blog on all things R and Data Science by Martin Chan. Topics covered include comparing dplyr and data.table, Shiny apps, ggplot, data cleaning, using RStudio, interviews with other R users/data scientists, and web scraping. +- [rweekly](https://rweekly.org/about) - Weekly Updates from the Entire R Community by Bruce Zhao, Colin Fay, Eric Nantz, Hao Zhu, Jon Calder, Jonathan Carroll, Maëlle Salmon, Ryo Nakagawara, and Wolfram Qin. +- [r-bloggers](https://www.r-bloggers.com/) - R-Bloggers.com was created by Tal Galili and is a blog aggregator of content contributed by bloggers who write about R (in English). The site helps R bloggers and users to connect and follow the R blogosphere. +- [Ryo Nakagawara](https://ryo-n7.github.io/) - Ryo Nakagawara is a Data Scientist and has been doing work as both a reporting analyst and a software developer in R and SQL to improve ACDI and VOCA data pipelines, create R packages, reproducible reports, dashboards, and Shiny apps to communicate how his projects worldwide are progressing. +- [Statistics Globe](https://statisticsglobe.com/) - Joachim Schork started this platform to share his statistical know-how and to improve his own statistical skills by discussing with other statisticians and programmers. +- [Stats and R](https://www.statsandr.com/) - Through his blog, Antoine Soetewey (PhD in statistics) aims at helping academics and professionals working with data to grasp important statistical concepts, and shows how to apply them in R. ## Books -- [A Sufficient Introduction to R](https://dereksonderegger.github.io/570L/) - 这本书旨在引导完全不熟悉编程的人沿着使用 R 的道路走向有用的技能水平.作者：Derek L. Sonderegger. -- [An Introduction to Statistical Learning](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf) - 本书介绍了统计学习方法. 作者：Gareth James、Daniela Witten、Trevor Hastie 和 Robert Tibshirani. -- [Advanced R](https://adv-r.hadley.nz/introduction.html) - 本书专为想要加深对语言的理解的 R 程序员，以及有其他语言经验、想要了解 R 与众不同之处的程序员而设计. [Exercise Solutions](https://advanced-r-solutions.rbind.io/) 作者：哈德利·威克姆. -- [An Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) - 本 R 简介源自 Bill Venables 和 David M. Smith 在阿德莱德大学时于 1990–2 年撰写的描述 S 和 S-Plus 环境的原始笔记集. -- [An Introduction to R](https://intro2r.com/) - 本书的目的是向您介绍如何使用 R，这是一种用于统计计算和研究的强大而灵活的交互式环境. 作者：Alex Douglas、Deon Roos、Francesca Mancini、Ana Couto 和 David Lusseau -- [Answering Questions with Data](https://crumplab.github.io/statistics/) - 这是一本免费教科书，为心理学本科生教授统计学入门知识. 这本教科书的编写考虑到了数学恐惧症，并试图减少与算术计算相关的恐惧症. 作者：马修·JC·克鲁普. -- [Data Science in a Box](https://datasciencebox.org/index.html) - 课程的核心内容侧重于数据采集和整理、探索性数据分析、数据可视化、推理、建模和结果的有效交流. -- [Data Science in Education Using R](https://datascienceineducation.com/) - 这本书主要是关于学习使用 R 作为教育数据科学的工具. 作者：Ryan A. Estrellado、Emily A. Bovee、Jesse Mostipak、Joshua M. Rosenberg 和 Isabella C. Velásquez. -- [Efficient R programming](https://csgillespie.github.io/efficientR/) - 高效的 R 编程是指在给定的时间内增加使用 R 可以完成的工作量. 它与计算和程序员效率有关. 作者：科林吉莱斯皮，罗宾洛夫莱斯. -- [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/) - 这本书涵盖了构建 Shiny 应用程序的过程，该应用程序稍后将被发送到生产环境. 作者：Colin Fay、Sébastien Rochette、Vincent Guyader、Cervan Girard. -- [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/) - 这本书涵盖了使用 R 汇总数据的基本探索技术.这些技术通常在正式建模开始之前应用，并且可以帮助为更复杂的统计模型的开发提供信息. 作者：Roger D. Peng. -- [Forecasting: Principles and Practice](https://otexts.com/fpp3/) - 本教科书旨在全面介绍预测方法，并提供有关每种方法的足够信息，以便读者能够明智地使用它们. 作者：Rob J Hyndman 和 George Athanasopoulos. -- [Geocomputation with R](https://geocompr.robinlovelace.net/) - 这本书是关于利用计算机的力量来处理地理数据的. 它教授一系列空间技能，包括阅读、写作和处理地理数据； 制作静态和交互式地图； 应用地理计算解决现实世界的问题； 和建模地理现象. 作者：Robin Lovelace、Jakub Nowosad、Jannes Muenchow. -- [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/index.html) - 这本书提供了对 ggplot2 的动手介绍，其中包含大量示例代码和图形. 它还解释了 ggplot2 所基于的语法. 作者：哈德利·威克姆. -- [Happy Git and GitHub for the useR](https://happygitwithr.com/) - Happy Git 提供关于如何安装 Git 并使其与 GitHub 顺利工作的自以为是的说明，在 shell 和 RStudio IDE 中，开发一些涵盖最常见任务的关键工作流程，并将 Git 和 GitHub 集成到您的日常工作中R 和 R 降价. 作者：Jenny Bryan、STAT 545 助教、Jim Hester. -- [Introduction to Data Science - Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/) - 这本书最初是作为 HarvardX 数据科学系列中使用的课堂笔记. 它介绍了可以帮助您应对现实世界数据分析挑战的概念和技能. 它涵盖了概率、统计推断、线性回归和机器学习的概念. 它还可以帮助您培养技能，例如 R 编程、使用 dplyr 进行数据整理、使用 ggplot2 进行数据可视化、使用 caret 构建算法、使用 UNIX/Linux shell 进行文件组织、使用 Git 和 GitHub 进行版本控制，以及使用 knitr 和 R markdown 准备可重现的文档. 作者：Rafael A. Irizarry 教授. -- [Introduction to Probability and Statistics Using R](http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/IPSUR.pdf) - 本书可分为三个基本部分. 第一部分包括引言和基本的描述性统计； 我希望学生们一开始就深入研究数据. 第二部分是概率研究，它从集合和等概率模型的基础开始，经过离散/连续随机变量，然后继续到多元分布. 关于抽样分布的章节为第三部分铺平了道路，即推论统计. 最后一部分包括点估计和区间估计、假设检验，最后介绍了应用统计学中的选定主题. 作者：G.杰伊克恩斯. -- [Introduction to R & Spatial Data with Raster and Terra](https://rspatial.org/intr/index.html) - 本文档提供了对 R 的简明介绍.它强调了您需要了解的内容才能在任何上下文中使用该语言. 作者：Robert Hijmans 教授. -- [JavaScript for R](https://book.javascript-for-r.com/) - 这项工作的最终目的是向读者展示通过将 JavaScript 引入他们的数据科学工作流程可以获得的许多巨大好处. 作者：约翰·科恩. -- [Learning Statistics with R](https://learningstatisticswithr.com/) - Learning Statistics with R 涵盖介绍性统计课程的内容，通常向本科心理学学生讲授，重点是 R 统计软件的使用. 作者：丹妮尔纳瓦罗. -- [Mastering Shiny](https://mastering-shiny.org/) - 这是 Mastering Shiny 的在线版本，这本书目前处于早期开发阶段，计划于 2020 年底发布. 本书补充了 [Shiny online documentation](https://shiny.rstudio.com/) 旨在帮助应用程序作者更深入地了解 Shiny. 作者：哈德利·威克姆. [Mastering Shiny Exercise solutions](https://mastering-shiny-solutions.org/index.html) -- [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/) - 第 1 章到第 7 章的想法是让您尽快高效地使用 R，尤其是在您已经具备编程知识的情况下. 从第 8 章开始，您将学习更多高级主题，尤其是使用 R 进行编程.作者：Bruno Rodrigues. -- [Modern Statistics with R](http://www.modernstatisticswithr.com/) - 从争论和探索数据到推理和预测建模. 本书包含大量示例和 200 多个带有可行解决方案的练习. 作者：Måns Thulin. -- [Practical Data Science with R](https://www.manning.com/books/practical-data-science-with-r-second-edition#toc) - 本书的目的是从务实的、面向实践的角度介绍数据科学. 本书专注于数据科学的过程，从项目的规划阶段，通过数据收集和探索，到建模，最后到部署和结果共享. 作者：Nina Zumel 和 John Mount. -- [Practical Regression and Anova using R](https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf) - 本文的重点是回归和方差分析的实践. 目的是了解可用的方法，更重要的是，了解何时应用这些方法. 作者：朱利安法拉维. -- [Practicals and Exercises](http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/practicalsBookNoAns.pdf) - 本系列练习回顾了作者讲座中讨论的一些内容，并介绍了有关在 R 中处理数据的其他一些基本概念.作者：Charles DiMaggio，博士. -- [Quantitative Politics with R](http://qpolr.com/qpolr.pdf) - 本书的目的是为 R 提供易于理解的介绍 -收集、研究和展示不同类型的政治数据. 作者：Erik Gahner Larsen 和 Zoltán Fazekas. -- [R Cookbook, 2nd Edition](https://rc2e.com/index.html) - 这本书充满了操作指南，每一个都解决了一个特定的问题. 该秘诀包括对解决方案的快速介绍，然后是旨在解开解决方案并让您深入了解其工作原理的讨论. 作者：James (JD) Long 和 Paul Teetor. -- [R for Data Science](https://r4ds.had.co.nz/) - 这本书将教您如何使用 R 进行数据科学.您将学习如何将数据导入 R，将其转化为最有用的结构，对其进行转换、可视化和建模. [Exercise Solutions](https://jrnold.github.io/r4ds-exercise-solutions/) 作者：Garrett Grolemund 和 Hadley Wickham. -- [R Packages](http://r-pkgs.had.co.nz/) - 在本书中，您将学习如何将您的代码变成其他人可以轻松下载和使用的包. 作者：哈德利·威克姆. -- [R Programming for Data Science](https://leanpub.com/rprogramming) - 这本书为您介绍了 R 编程的基础知识，使用了作为行业领先的约翰霍普金斯数据科学专业化的一部分开发的相同材料. 作者：罗杰彭. -- [R Tutorial – Be a Data Science rock star with R](https://data-flair.training/blogs/r-tutorial/) - R 编程语言之旅，探索其不同的基本概念. 这个 R DataFlair 教程系列旨在帮助初学者开始使用 R 并帮助有经验的人提高他们的 R 编程技能并在语言中获得完美. -- [Statistical Inference via Data Science](https://moderndive.com/) - 这旨在温和地介绍以数据科学家、统计学家、数据记者和其他研究人员的方式分析数据和使用数据回答问题的实践. 作者：Chester Ismay 和 Albert Y. Kim. -- [Supervised Machine Learning for Text Analysis in R](https://smltar.com/) - 本书侧重于文本的监督或预测建模，使用文本数据对我们周围的世界进行预测. 作者：Emil Hvitfeldt 和 Julia Silge. -- [Text Mining with R](https://www.tidytextmining.com/) - 这本书介绍了如何使用 tidytext 包和 R 中的其他整洁工具进行文本挖掘.作者：Julia Silge 和 David Robinson. -- [The Art of R Programming](http://diytranscriptomics.com/Reading/files/The%20Art%20of%20R%20Programming.pdf) - 这本书是为那些希望学习用 R 开发软件的人准备的.作者：Norman Matloff. -- [The Book of R](https://web.itu.edu.tr/~tokerem/The_Book_of_R.pdf) - The Book of R: A First Course in Programming and Statistics 的目的是提供一个相对温和但信息丰富的统计软件环境 R 以及一些常见的统计分析，以便读者可以有一个坚实的基础，最终成为自己的专家. [Exercise solutions](https://nostarch.com/bookofr) 作者：蒂尔曼·M·戴维斯. -- [The R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf) - 一本关于 R 中的麻烦点、怪事、陷阱和故障的书.作者：Patrick Burns. -- [The R Language](https://stat.ethz.ch/R-manual/R-patched/doc/html/) - 由 R 语言的作者编写的 R 简介. -- [Tidy Modeling with R](https://www.tmwr.org/) - 本书是使用 R 编程语言中的一组新软件进行模型构建的指南. +- [A Sufficient Introduction to R](https://dereksonderegger.github.io/570L/) - This book is intended to guide people that are completely new to programming along a path towards a useful skill level using R. Author: Derek L. Sonderegger. +- [An Introduction to Statistical Learning](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf) - This book provides an introduction to statistical learning methods. Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. +- [Advanced R](https://adv-r.hadley.nz/introduction.html) - This book is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. [Exercise Solutions](https://advanced-r-solutions.rbind.io/) Author: Hadley Wickham. +- [An Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) - This introduction to R is derived from an original set of notes describing the S and S-Plus environments written in 1990–2 by Bill Venables and David M. Smith when at the University of Adelaide. +- [An Introduction to R](https://intro2r.com/) - The aim of this book is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. Authors: Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau +- [Answering Questions with Data](https://crumplab.github.io/statistics/) - This is a free textbook teaching introductory statistics for undergraduates in Psychology. The textbook was written with math-phobia in mind and attempts to reduce the phobia associated with arithmetic computations. Author: Matthew J. C. Crump. +- [Data Science in a Box](https://datasciencebox.org/index.html) - The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results. +- [Data Science in Education Using R](https://datascienceineducation.com/) - This book is primarily about learning to use R as a tool for data science in education. Authors: Ryan A. Estrellado, Emily A. Bovee, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez. +- [Efficient R programming](https://csgillespie.github.io/efficientR/) - Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. Authors: Colin Gillespie, Robin Lovelace. +- [Engineering Production-Grade Shiny Apps](https://engineering-shiny.org/) - This book covers the process of building a Shiny application that will later be sent to production. Authors: Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard. +- [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/) - This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Author: Roger D. Peng. +- [Forecasting: Principles and Practice](https://otexts.com/fpp3/) - This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. Authors: Rob J Hyndman and George Athanasopoulos. +- [Geocomputation with R](https://geocompr.robinlovelace.net/) - This book is about using the power of computers to do things with geographic data. It teaches a range of spatial skills, including reading, writing and manipulating geographic data; making static and interactive maps; applying geocomputation to solve real-world problems; and modeling geographic phenomena. Authors: Robin Lovelace, Jakub Nowosad, Jannes Muenchow. +- [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/index.html) - This book provides a hands-on introduction to ggplot2 with lots of example code and graphics. It also explains the grammar on which ggplot2 is based. Author: Hadley Wickham. +- [Happy Git and GitHub for the useR](https://happygitwithr.com/) - Happy Git provides opinionated instructions on how to install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE, develop a few key workflows that cover your most common tasks, and integrate Git and GitHub into your daily work with R and R Markdown. Authors: Jenny Bryan, the STAT 545 TAs, Jim Hester. +- [Introduction to Data Science - Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/) - This book started out as the class notes used in the HarvardX Data Science Series. It introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. Author: Professor Rafael A. Irizarry. +- [Introduction to Probability and Statistics Using R](http://www.atmos.albany.edu/facstaff/timm/ATM315spring14/R/IPSUR.pdf) - The book can be subdivided into three basic parts. The first part includes the introductions and elementary descriptive statistics; I want the students to be knee-deep in data right out of the gate. The second part is the study of probability, which begins at the basics of sets and the equally likely model, journeys past discrete/continuous random variables, and continues through to multivariate distributions. The chapter on sampling distributions paves the way to the third part, which isinferential statistics. This last part includes point and interval estimation, hypothesis testing, and finishes with introductions to selected topics in applied statistics. Author: G. Jay Kerns. +- [Introduction to R & Spatial Data with Raster and Terra](https://rspatial.org/intr/index.html) - This document provides a concise introduction to R. It emphasizes what you need to know to be able to use the language in any context. Author: Professor Robert Hijmans. +- [JavaScript for R](https://book.javascript-for-r.com/) - The ultimate aim of this work is to demonstrate to the reader the many great benefits one can reap by inviting JavaScript into their data science workflow. Author: John Coene. +- [Learning Statistics with R](https://learningstatisticswithr.com/) - Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. Author: Danielle Navarro. +- [Mastering Shiny](https://mastering-shiny.org/) - This is the online version of Mastering Shiny, a book currently under early development and intended for a late 2020 release. This book complements the [Shiny online documentation](https://shiny.rstudio.com/) and is intended to help app authors develop a deeper understanding of Shiny. Author: Hadley Wickham. [Mastering Shiny Exercise solutions](https://mastering-shiny-solutions.org/index.html) +- [Modern R with the tidyverse](https://b-rodrigues.github.io/modern_R/) - The idea of Chapters 1 to 7 is to make you efficient with R as quickly as possible, especially if you already have prior programming knowledge. Starting with Chapter 8 you will learn more advanced topics, especially programming with R. Author: Bruno Rodrigues. +- [Modern Statistics with R](http://www.modernstatisticswithr.com/) - From wrangling and exploring data to inference and predictive modelling. The book includes plenty of examples and more than 200 exercises with worked solutions. Author: Måns Thulin. +- [Practical Data Science with R](https://www.manning.com/books/practical-data-science-with-r-second-edition#toc) - The intent of this book is to present data science from a pragmatic, practice-oriented viewpoint. The book concentrates on the process of data science, from the planning stages of a project, through the data collection and exploration, to the modeling, and finally to deployment and the sharing of results. Authors: Nina Zumel and John Mount. +- [Practical Regression and Anova using R](https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf) - The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. Author: Julian Faraway. +- [Practicals and Exercises](http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/practicalsBookNoAns.pdf) - This series of exercises reviews some of the content discussed during the author's lectures, and introduces some other basic concepts about working with data in R. Author: Charles DiMaggio, PhD. +- [Quantitative Politics with R](http://qpolr.com/qpolr.pdf) - The aim of this book is to provide an easily accessible introduction to R for the +collection, study and presentation of different types of political data. Authors: Erik Gahner Larsen and Zoltán Fazekas. +- [R Cookbook, 2nd Edition](https://rc2e.com/index.html) - This book is full of how-to recipes, each of which solves a specific problem. The recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. Authors: James (JD) Long and Paul Teetor. +- [R for Data Science](https://r4ds.had.co.nz/) - This book will teach you how to do data science with R. You will learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it. [Exercise Solutions](https://jrnold.github.io/r4ds-exercise-solutions/) Authors: Garrett Grolemund and Hadley Wickham. +- [R Packages](http://r-pkgs.had.co.nz/) - In this book you will learn how to turn your code into packages that others can easily download and use. Author: Hadley Wickham. +- [R Programming for Data Science](https://leanpub.com/rprogramming) - This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. Author: Roger Peng. +- [R Tutorial – Be a Data Science rock star with R](https://data-flair.training/blogs/r-tutorial/) - A tour of the R programming language that explores its different and essential concepts. This R DataFlair Tutorial Series is designed to help beginners to get started with R and experienced to brush up their R programming skills and gain perfection in the language. +- [Statistical Inference via Data Science](https://moderndive.com/) - This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would. Authors: Chester Ismay and Albert Y. Kim. +- [Supervised Machine Learning for Text Analysis in R](https://smltar.com/) - This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. Authors: Emil Hvitfeldt and Julia Silge. +- [Text Mining with R](https://www.tidytextmining.com/) - This book serves as an introduction of text mining using the tidytext package and other tidy tools in R. Authors: Julia Silge and David Robinson. +- [The Art of R Programming](http://diytranscriptomics.com/Reading/files/The%20Art%20of%20R%20Programming.pdf) - This book is for those who wish to learn about developing software in R. Author: Norman Matloff. +- [The Book of R](https://web.itu.edu.tr/~tokerem/The_Book_of_R.pdf) - The aim of The Book of R: A First Course in Programming and Statistics is to provide a relatively gentle yet informative exposure to the statistical software environment R, alongside some common statistical analyses, so that readers may have a solid foundation from which to eventually become experts in their own right. [Exercise solutions](https://nostarch.com/bookofr) Author: Tilman M. Davies. +- [The R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf) - A book about trouble spots, oddities, traps, and glitches in R. Author: Patrick Burns. +- [The R Language](https://stat.ethz.ch/R-manual/R-patched/doc/html/) - An introduction to R written by the authors of the R language. +- [Tidy Modeling with R](https://www.tmwr.org/) - This book is a guide to using a new collection of software in the R programming language for model building. ## Communities of Practice -> 实践社区是一群人，他们对所做的事情有共同的关注或热情，并在定期互动时学习如何做得更好. +> A community of practice is a group of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly. -- [TidyTuesday](https://github.com/rfordatascience/tidytuesday) - TidyTuesday 是一个针对 R 生态系统的每周数据项目，重点放在了解如何总结和安排数据以制作有意义的图表. -- [R for Data Science (R4DS) Online Learning Community](https://www.rfordatasci.com/) - 由 Jessie Mostipak (@kierisi) 创立，旨在为学习者和导师创建一个支持和响应迅速的在线空间，以收集和学习 Garrett Grolemund 和 Hadley Wickham 编写的 R for Data Science 一书. 成长为一个由各种技能水平的 R 学习者组成的社区，他们共同努力提高他们的技能. +- [TidyTuesday](https://github.com/rfordatascience/tidytuesday) - TidyTuesday is a weekly data project aimed at the R ecosystem with an emphasis placed on understanding how to summarize and arrange data to make meaningful charts. +- [R for Data Science (R4DS) Online Learning Community](https://www.rfordatasci.com/) - Founded by Jessie Mostipak (@kierisi) to create a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science book by Garrett Grolemund and Hadley Wickham. Grown into a community of R learners at all skill levels working together to improve their skills. ## Podcasts -- [Not so Standard Deviations](http://nssdeviations.com/) - 一个数据科学播客，Roger Peng 和 Hilary Parker 谈论学术界和工业界最新的数据科学和数据分析. -- [The R-Podcast](https://r-podcast.org/) - 关于如何利用 R 完成创新和强大的数据分析的实用建议. 由 Eric Nantz 主持. +- [Not so Standard Deviations](http://nssdeviations.com/) - A data science podcast where Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry. +- [The R-Podcast](https://r-podcast.org/) - Practical advice on how to take advantage of R to accomplish innovative and robust data analyses. Hosted by Eric Nantz. ## YouTube -- [Andrew Couch](https://www.youtube.com/channel/UCnwYO3Sz_emBTC1sTZ6TlsQ) - 主题包括建模、创建函数、仪表板和预测. -- [Ben Stenhaug](https://www.youtube.com/user/benastenhaug/videos) - 主题包括保存和读取数据、purrr 中的地图函数、t 检验、项目响应理论以及 R 和 tidyverse 的基础知识. -- [Cédric Scherer](https://www.youtube.com/playlist?list=PLd6eTXMmV3X-4-pHkZSJwHRACzSSyeT9T) - 关于 R 相关主题（如 ggplot2 或 Shiny）以及一般数据可视化的一系列讲座和研讨会. -- [Colin Quirk](https://www.youtube.com/channel/UC-vtwz7ueU2dtnHk5e-WblA) - 主题包括正则表达式、数据类型、Shiny 和 gganimate. -- [Data Analysis and Visualization Using R](https://www.youtube.com/channel/UClLf9MZuUy89IwGtRHC0RzQ) - 使用 R 进行数据分析和可视化在线课程的主题. -- [Data Science with Tom](https://www.youtube.com/channel/UCb5aI-GwJm3ZxlwtCsLu78Q) - 主题包括时间序列、使用 ggraph 和 tidytext 分析单词关系，以及 tidymodels. -- [David Jablonski](https://www.youtube.com/channel/UCzE7zgPikKvVUJPBKrndHMA) - UC Berkeley R Bootcamp 播放列表包括有关 R 基础知识、处理数据、执行计算、编程、图形、工作流程和统计的视频. -- [David Robinson](https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ) - 主题包括 EDA 绘图、数据操作、动画映射、可视化、文本挖掘、时间序列、预测、回归、自举、包开发、网络图、ANOVA、JSON、模拟、生存分析和 tidymetrics. 点击 [here](https://github.com/dgrtwo/data-screencasts/tree/master/screencast-annotations) 获取详细的 TidyTuesday 截屏视频注释. -- [Dean Attali](https://www.youtube.com/c/DAattali/videos) - Shiny，包括几个关于调试 Shiny 的视频. -- [Dragonfly Statistics](https://www.youtube.com/c/DragonflyStatistics/videoss) - 主题包括数值计算、生成随机游走、马尔可夫链、编码分类变量、概率、相关图、特征工程、时间序列、二元分类器、模型、数据表、混淆矩阵、机器学习、地理编码、汇总统计和模拟. -- [IDG TECHtalk](https://www.youtube.com/playlist?list=PL7D2RMSmRO9JOvPC1gbA8Mc3azvSfm8Vv) - Do More with R 播放列表包括有关 shiny、data.table、获取 API 数据、将 Git 和 Github 与 R 结合使用、编写自己的包、在 R 代码中运行 Python、RStudio 插件和键盘快捷键、仪表板和 flexdashboards 的教程. -- [Julia Silge](https://www.youtube.com/channel/UCTTBgWyJl2HrrhQOOc710kA) - 主题包括预测文本建模、估算缺失数据、tidymodels、情感分析、多项分类、主成分分析、数据预处理和重采样以及多项分类. -- [Lander Analytics](https://www.youtube.com/channel/UC2-hKemnrmVCH_29duyJ26A/videos) - 不同专家就各种主题进行深入讨论. -- [MarinStatsLectures](https://www.youtube.com/c/marinstatlectures/featured) - 主题包括描述性统计、方差分析、自举、线性回归、双变量分析和概率分布. -- [Numyard](https://www.youtube.com/c/TheLearnR/videos) - 主题包括使用数据帧、for 循环、基础数学、向量、列表、创建函数、数据类型和随机抽样. -- [R Programming 101](https://www.youtube.com/c/RProgramming101/featured) - 本频道提供使用R编程进行数据分析和统计分析的教学视频. 教学视频包括数据清洗、数据操作、数据可视化、统计分析、机器学习和AI（人工智能）等主题. -- [Richard Webster](https://www.youtube.com/channel/UC5ktyacv_aPSBmKB7uX5Piw/videos) - 主题包括粘贴函数、应用函数系列、while 和 for 循环、条件语句、可视化、删除 NA 和组合数据. -- [RichardOnData](https://www.youtube.com/playlist?list=PLOKCg4WX8ZG4nboHnOgA8PJxGWnO4csiZ) - R 播放列表包括有关使用 dplyr 处理数据、使用 ggplot2 和 ggThemeAssist 可视化数据、数据类型和结构、重要的基本 r 函数、使用 lubridate 处理日期时间、使用 forcats 克服因素、使用 stringr 处理文本的视频. -- [Shiny Developer Series](https://www.youtube.com/c/ShinyDeveloperSeries/videos) - Shiny Developer Series 的目标是展示不断发展的 Shiny 生态系统中的创新应用程序和软件包，以及它们背后的杰出开发人员！ -- [Simplilearn](https://www.youtube.com/playlist?list=PLEiEAq2VkUUKAw0aAJ1W4jpZ1q9LpX4yG) - R Programming for Beginners 播放列表包括有关数据科学、图表、数据可视化、算法、业务分析、回归、随机森林、SVM、聚类、时间序列、建模和分析技术的视频. -- [Statistics Globe](https://www.youtube.com/channel/UCyHEww8_SCdxZvEnkCfi55w) - 一系列简短但详细的教程，介绍如何解决您在使用 R 时遇到的常见问题.主题包括数据格式化、重新排序数据、字符串和 ggplot2. -- [StatistikinDD](https://www.youtube.com/c/StatistikinDD/featured) - 关于高效 R 编程（例如并行运行 R 代码）、可视化、回归分析的播放列表. +- [Andrew Couch](https://www.youtube.com/channel/UCnwYO3Sz_emBTC1sTZ6TlsQ) - Topics include modeling, creating functions, dashboards, and forecasting. +- [Ben Stenhaug](https://www.youtube.com/user/benastenhaug/videos) - Topics include saving and reading data, map functions in purrr, t-tests, item response theory, and the basics of R and the tidyverse. +- [Cédric Scherer](https://www.youtube.com/playlist?list=PLd6eTXMmV3X-4-pHkZSJwHRACzSSyeT9T) - A collection of talks and seminars about R-related topics such as ggplot2 or Shiny, and data visualization in general. +- [Colin Quirk](https://www.youtube.com/channel/UC-vtwz7ueU2dtnHk5e-WblA) - Topics include regular expressions, data types, Shiny, and gganimate. +- [Data Analysis and Visualization Using R](https://www.youtube.com/channel/UClLf9MZuUy89IwGtRHC0RzQ) - Topics for the online course Data Analysis and Visualization Using R. +- [Data Science with Tom](https://www.youtube.com/channel/UCb5aI-GwJm3ZxlwtCsLu78Q) - Topics include time series, analyzing word relationships with ggraph and tidytext, and tidymodels. +- [David Jablonski](https://www.youtube.com/channel/UCzE7zgPikKvVUJPBKrndHMA) - The UC Berkeley R Bootcamp playlists include videos on R basics, handling data, performing calculations, programming, graphics, workflows, and statistics. +- [David Robinson](https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ) - Topics include graphing for EDA, data manipulation, animated mapping, visualization, text mining, time series, forecasting, regression, bootstrapping, package development, network graphs, ANOVA, JSON, simulation, survival analysis, and tidymetrics. Click [here](https://github.com/dgrtwo/data-screencasts/tree/master/screencast-annotations) for detailed TidyTuesday screencast annotations. +- [Dean Attali](https://www.youtube.com/c/DAattali/videos) - Shiny, including several videos on debugging Shiny. +- [Dragonfly Statistics](https://www.youtube.com/c/DragonflyStatistics/videoss) - Topics include numerical computing, generating random walks, markov chains, encoding categorical variables, probability, correlation plots, feature engineering, time series, binary classifiers, models, data.table, confusion matrices, machine learning, geocoding, summary statistics, and simulation. +- [IDG TECHtalk](https://www.youtube.com/playlist?list=PL7D2RMSmRO9JOvPC1gbA8Mc3azvSfm8Vv) - Do More with R playlist includes tutorials on shiny, data.table, getting API data, using Git and Github with R, writing your own packages, run Python in R code, RStudio addins and keyboard shortcuts, dashboards and flexdashboards. +- [Julia Silge](https://www.youtube.com/channel/UCTTBgWyJl2HrrhQOOc710kA) - Topics include predictive text modeling, impute missing data, tidymodels, sentiment analysis, multinomial classification, principal component analysis, data preprocessing and resampling, and multinomial classification. +- [Lander Analytics](https://www.youtube.com/channel/UC2-hKemnrmVCH_29duyJ26A/videos) - In-depth talks by different experts on a wide variety of topics. +- [MarinStatsLectures](https://www.youtube.com/c/marinstatlectures/featured) - Topics include descriptive statistics, ANOVA, bootstrapping, linear regression, bivariate analysis, and probability distributions. +- [Numyard](https://www.youtube.com/c/TheLearnR/videos) - Topics include working with dataframes, for loops, basic math, vectors, lists, creating functions, data types, and random sampling. +- [R Programming 101](https://www.youtube.com/c/RProgramming101/featured) - This channel provides teaching videos on data analysis and statistical analysis using R programming. The teaching videos include subjects like data cleaning, data manipulation, data visualization, statistical analysis, and machine learning and AI (artificial intelligence). +- [Richard Webster](https://www.youtube.com/channel/UC5ktyacv_aPSBmKB7uX5Piw/videos) - Topics include the paste function, the apply family of functions, while and for loops, conditional statements, visualization, removing NAs, and combining data. +- [RichardOnData](https://www.youtube.com/playlist?list=PLOKCg4WX8ZG4nboHnOgA8PJxGWnO4csiZ) - The R playlist includes videos on manipulating data with dplyr, visualizing data with ggplot2 and ggThemeAssist, data types and structures, important base r functions, handling datetimes with lubridate, conquering factors with forcats, manipulating text with stringr. +- [Shiny Developer Series](https://www.youtube.com/c/ShinyDeveloperSeries/videos) - The goals of the Shiny Developer Series are to showcase the innovative applications and packages in the ever-growing Shiny ecosystem, as well as the brilliant developers behind them! +- [Simplilearn](https://www.youtube.com/playlist?list=PLEiEAq2VkUUKAw0aAJ1W4jpZ1q9LpX4yG) - The R Programming for Beginners playlist includes videos on data science, charting, data visualization, algorithms, business analytics, regression, random forest, SVM, clustering, time series, modeling, and analytical techniques. +- [Statistics Globe](https://www.youtube.com/channel/UCyHEww8_SCdxZvEnkCfi55w) - A collection of short but detailed tutorials on how to work through common problems you will face while using R. Topics include data formatting, reordering data, strings, and ggplot2. +- [StatistikinDD](https://www.youtube.com/c/StatistikinDD/featured) - Playlists on Efficient R Programming (e. g. running R code in parallel), Visualization, Regression Analyses. - [StatQuest with Josh Starmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUJJpBNfk8_YadPwDTO2SCbx) - The Statistics and Machine Learning in R playlist deals with principal component analysis, random forest, regression, ROC and AUC, and ridge, lasso and elastic-net. -- [TidyX](https://www.youtube.com/channel/UCP8l94xtoemCH_GxByvTuFQ) - TidyX 是一个截屏，主持人从 TidyTuesday 项目中选择代码并逐行查看他们的代码，解释他们做了什么以及他们使用的功能如何工作. 他们还分解了他们创建的可视化效果，并讨论了如何将类似的方法应用于其他数据集. 目标是帮助更多人学习 R 并参与 TidyTuesday 社区. +- [TidyX](https://www.youtube.com/channel/UCP8l94xtoemCH_GxByvTuFQ) - TidyX is a screen cast where the hosts select code from the TidyTuesday project and go through their code line-by-line, explaining what they did and how the functions they used work. They also break down the visualizations they create and talk about how to apply similar approaches to other data sets. The objective is to help more people learn R and get involved in the TidyTuesday community. ## Contributing - - 随时欢迎您的贡献！ 请访问我们 [contributing.md](https://github.com/iamericfletcher/r-learning-resources/blob/main/contributing.md) 了解如何为这个列表做出贡献. +- Your contributions are always welcome! Please visit our [contributing.md](https://github.com/iamericfletcher/r-learning-resources/blob/main/contributing.md) to learn how to contribute to this list. diff --git a/docs/awesome/awesome-rust.md b/docs/awesome/awesome-rust.md index e97b2d8585..6d5d0e90ea 100644 --- a/docs/awesome/awesome-rust.md +++ b/docs/awesome/awesome-rust.md @@ -1506,6 +1506,7 @@ See also [Are we game yet?](https://arewegameyet.rs) * cute * [mattgathu/cute](https://github.com/mattgathu/cute) - Macro for Python-esque list comprehensions. +* [elastio/bon](https://github.com/elastio/bon) [[bon](https://crates.io/crates/bon)] - generate compile-time-checked builders for structs and functions, provides partial application, optional and named parameters for functions and methods. [![build status](https://github.com/elastio/bon/actions/workflows/ci.yml/badge.svg)](https://github.com/elastio/bon/actions) * [Linq-in-Rust](https://github.com/StardustDL/Linq-in-Rust) - Macro and methods for C#-LINQ-like expressions. [![CI](https://github.com/StardustDL/Linq-in-Rust/workflows/CI/badge.svg?branch=master)](https://github.com/StardustDL/Linq-in-Rust/actions?query=workflow%3ACI) ### Markup language diff --git a/docs/awesome/awesome-testing.md b/docs/awesome/awesome-testing.md index df92a801ce..83e434caad 100644 --- a/docs/awesome/awesome-testing.md +++ b/docs/awesome/awesome-testing.md @@ -21,6 +21,7 @@ Finally, I'm sure everyone who reads this list has one thing they want to add. P ### Make your life easier - [LambdaTest](https://www.lambdatest.com) - An AI-powered unified enterprise test execution cloud platform that helps businesses drastically reduce time to market through faster test execution, ensuring quality releases and accelerated digital transformation - [GoodLooks](https://github.com/dashcamio/goodlooks) - Visually validate Playwright tests using AI vision instead of flaky selectors. +- [TestingBot](https://testingbot.com) - Run automated, manual and visual tests on a wide range of browsers and devices. - [Octomind](https://github.com/OctoMind-dev) - auto-generated, run and maintained Playwright tests with AI-powered test case discovery. - [Courgette](https://courgette-testing.com) - Beautifully simple UI testing. Proper declarative BDD scenarios using Gherkin, Gherkin templates and composable YAML-style page and component objects. - [Ferrum](https://github.com/rubycdp/ferrum) - very simple and easy to get started with frontend/UI testing in small Ruby scripts, high-level API to control Chrome with the CDP - Chrome DevTools Protocol (NO Selenium dependency)